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 "cells": [
  {
   "source": [
    "# PET-CT Registration Practice"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Purpose of This Practice\n",
    "- Study **dicom** Medical Image Files.\n",
    "- Experience real **PET-CT** dataset.\n",
    "\n",
    "### RIDER Lung PET-CT Dataset\n",
    "##### Reference : https://wiki.cancerimagingarchive.net/display/Public/RIDER+Lung+PET-CT#225127500af19c1c2e444b1abb1cc196e52abae6"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "# I. Data Preprocessing\n",
    "## Contents\n",
    "0. Import / Setup\n",
    "1. PET-CT Metadata Preprocessing\n",
    "2. Data Selection\n",
    "3. Voxel Generation"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 0. Import / Setup"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import shutil\n",
    "import math\n",
    "from tqdm.notebook import tqdm\n",
    "import time\n",
    "from glob import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pydicom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "D:\\manifest-TWT4rfni3436550357686601184\n"
     ]
    }
   ],
   "source": [
    "# PATH Setting\n",
    "DISK_PATH = 'D:\\\\'\n",
    "META_PATH = 'manifest-TWT4rfni3436550357686601184'\n",
    "DIR_PATH = os.path.join(DISK_PATH, META_PATH)\n",
    "print (DIR_PATH)"
   ]
  },
  {
   "source": [
    "## 1. PET-CT Metadata Preprocessing"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 1) Data Loading"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                             Series UID         Collection  \\\n",
       "0     1.3.6.1.4.1.9328.50.17.21539635004042802696358...  RIDER Lung PET-CT   \n",
       "1     1.3.6.1.4.1.9328.50.17.24674249575175155363249...  RIDER Lung PET-CT   \n",
       "2     1.3.6.1.4.1.9328.50.17.14298831690972470748724...  RIDER Lung PET-CT   \n",
       "3     1.3.6.1.4.1.9328.50.17.33448411630762243528802...  RIDER Lung PET-CT   \n",
       "4     1.3.6.1.4.1.9328.50.17.13157237625811298431135...  RIDER Lung PET-CT   \n",
       "...                                                 ...                ...   \n",
       "1323  1.3.6.1.4.1.9328.50.17.29526182708906873966519...  RIDER Lung PET-CT   \n",
       "1324  1.3.6.1.4.1.9328.50.17.27647997212178938595947...  RIDER Lung PET-CT   \n",
       "1325  1.3.6.1.4.1.9328.50.17.29196707572056236162490...  RIDER Lung PET-CT   \n",
       "1326  1.3.6.1.4.1.9328.50.17.10888495592818845871887...  RIDER Lung PET-CT   \n",
       "1327  1.3.6.1.4.1.9328.50.17.29822011685214407434477...  RIDER Lung PET-CT   \n",
       "\n",
       "      3rd Party Analysis                           Data Description URI  \\\n",
       "0                    NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1                    NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "2                    NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "3                    NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "4                    NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "...                  ...                                            ...   \n",
       "1323                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1324                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1325                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1326                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1327                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "\n",
       "            Subject ID                                          Study UID  \\\n",
       "0     RIDER-1002189179  1.3.6.1.4.1.9328.50.17.74928344181377977351227...   \n",
       "1     RIDER-1002189179  1.3.6.1.4.1.9328.50.17.74928344181377977351227...   \n",
       "2     RIDER-1002189179  1.3.6.1.4.1.9328.50.17.74928344181377977351227...   \n",
       "3     RIDER-1002189179  1.3.6.1.4.1.9328.50.17.74928344181377977351227...   \n",
       "4     RIDER-1006487867  1.3.6.1.4.1.9328.50.17.10616571986483711586653...   \n",
       "...                ...                                                ...   \n",
       "1323  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "1324  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "1325  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "1326  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "1327  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "\n",
       "     Study Description  Study Date               Series Description  \\\n",
       "0                  NaN  05-07-2006                    CT 2.5mm cb03   \n",
       "1                  NaN  05-07-2006                  3D FBP H10 cb03   \n",
       "2                  NaN  05-07-2006  5min 3D OSEM 8it28sub 10MM cb03   \n",
       "3                  NaN  05-07-2006   5min 3D OSEM 8it28sub 7MM cb03   \n",
       "4       Unspecified CT  06-29-2006                              NaN   \n",
       "...                ...         ...                              ...   \n",
       "1323   FDG 6AFOV TORSO  02-02-2006                 CT SCOUT HEAD IN   \n",
       "1324   FDG 6AFOV TORSO  02-02-2006                       AC for PET   \n",
       "1325   FDG 6AFOV TORSO  02-02-2006           70CM FOV 3.27MM CT STD   \n",
       "1326   FDG 6AFOV TORSO  02-02-2006                      PET FDG SUV   \n",
       "1327   FDG 6AFOV TORSO  02-02-2006                      PET FDG NAC   \n",
       "\n",
       "            Manufacturer Modality                              SOP Class Name  \\\n",
       "0     GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "1     GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "2     GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "3     GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "4     GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "...                  ...      ...                                         ...   \n",
       "1323  GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "1324  GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "1325  GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "1326  GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "1327  GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "\n",
       "                    SOP Class UID  Number of Images  File Size  \\\n",
       "0       1.2.840.10008.5.1.4.1.1.2                64   32.12 MB   \n",
       "1     1.2.840.10008.5.1.4.1.1.128                47    1.59 MB   \n",
       "2     1.2.840.10008.5.1.4.1.1.128                47    1.59 MB   \n",
       "3     1.2.840.10008.5.1.4.1.1.128                47    1.59 MB   \n",
       "4       1.2.840.10008.5.1.4.1.1.2                 2    4.35 MB   \n",
       "...                           ...               ...        ...   \n",
       "1323    1.2.840.10008.5.1.4.1.1.2                 2    4.97 MB   \n",
       "1324    1.2.840.10008.5.1.4.1.1.2               429  215.30 MB   \n",
       "1325    1.2.840.10008.5.1.4.1.1.2               739  370.88 MB   \n",
       "1326  1.2.840.10008.5.1.4.1.1.128               267    9.08 MB   \n",
       "1327  1.2.840.10008.5.1.4.1.1.128               267    9.07 MB   \n",
       "\n",
       "                                          File Location  \\\n",
       "0     .\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...   \n",
       "1     .\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...   \n",
       "2     .\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...   \n",
       "3     .\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...   \n",
       "4     .\\RIDER Lung PET-CT\\RIDER-1006487867\\06-29-200...   \n",
       "...                                                 ...   \n",
       "1323  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "1324  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "1325  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "1326  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "1327  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "\n",
       "           Download Timestamp  \n",
       "0     2021-07-07T21:33:13.717  \n",
       "1     2021-07-07T21:31:39.064  \n",
       "2     2021-07-07T21:31:39.064  \n",
       "3     2021-07-07T21:31:45.154  \n",
       "4     2021-07-07T21:31:52.929  \n",
       "...                       ...  \n",
       "1323  2021-07-08T19:43:11.042  \n",
       "1324  2021-07-08T19:45:15.912  \n",
       "1325   2021-07-08T19:56:04.11  \n",
       "1326  2021-07-08T19:34:45.372  \n",
       "1327  2021-07-08T19:43:41.948  \n",
       "\n",
       "[1328 rows x 17 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Series UID</th>\n      <th>Collection</th>\n      <th>3rd Party Analysis</th>\n      <th>Data Description URI</th>\n      <th>Subject ID</th>\n      <th>Study UID</th>\n      <th>Study Description</th>\n      <th>Study Date</th>\n      <th>Series Description</th>\n      <th>Manufacturer</th>\n      <th>Modality</th>\n      <th>SOP Class Name</th>\n      <th>SOP Class UID</th>\n      <th>Number of Images</th>\n      <th>File Size</th>\n      <th>File Location</th>\n      <th>Download Timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.3.6.1.4.1.9328.50.17.21539635004042802696358...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1002189179</td>\n      <td>1.3.6.1.4.1.9328.50.17.74928344181377977351227...</td>\n      <td>NaN</td>\n      <td>05-07-2006</td>\n      <td>CT 2.5mm cb03</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>64</td>\n      <td>32.12 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...</td>\n      <td>2021-07-07T21:33:13.717</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.3.6.1.4.1.9328.50.17.24674249575175155363249...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1002189179</td>\n      <td>1.3.6.1.4.1.9328.50.17.74928344181377977351227...</td>\n      <td>NaN</td>\n      <td>05-07-2006</td>\n      <td>3D FBP H10 cb03</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>47</td>\n      <td>1.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...</td>\n      <td>2021-07-07T21:31:39.064</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.3.6.1.4.1.9328.50.17.14298831690972470748724...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1002189179</td>\n      <td>1.3.6.1.4.1.9328.50.17.74928344181377977351227...</td>\n      <td>NaN</td>\n      <td>05-07-2006</td>\n      <td>5min 3D OSEM 8it28sub 10MM cb03</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>47</td>\n      <td>1.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...</td>\n      <td>2021-07-07T21:31:39.064</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.3.6.1.4.1.9328.50.17.33448411630762243528802...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1002189179</td>\n      <td>1.3.6.1.4.1.9328.50.17.74928344181377977351227...</td>\n      <td>NaN</td>\n      <td>05-07-2006</td>\n      <td>5min 3D OSEM 8it28sub 7MM cb03</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>47</td>\n      <td>1.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...</td>\n      <td>2021-07-07T21:31:45.154</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.3.6.1.4.1.9328.50.17.13157237625811298431135...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1006487867</td>\n      <td>1.3.6.1.4.1.9328.50.17.10616571986483711586653...</td>\n      <td>Unspecified CT</td>\n      <td>06-29-2006</td>\n      <td>NaN</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>2</td>\n      <td>4.35 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1006487867\\06-29-200...</td>\n      <td>2021-07-07T21:31:52.929</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1323</th>\n      <td>1.3.6.1.4.1.9328.50.17.29526182708906873966519...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>CT SCOUT HEAD IN</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>2</td>\n      <td>4.97 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:43:11.042</td>\n    </tr>\n    <tr>\n      <th>1324</th>\n      <td>1.3.6.1.4.1.9328.50.17.27647997212178938595947...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>AC for PET</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>429</td>\n      <td>215.30 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:45:15.912</td>\n    </tr>\n    <tr>\n      <th>1325</th>\n      <td>1.3.6.1.4.1.9328.50.17.29196707572056236162490...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>70CM FOV 3.27MM CT STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>739</td>\n      <td>370.88 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:56:04.11</td>\n    </tr>\n    <tr>\n      <th>1326</th>\n      <td>1.3.6.1.4.1.9328.50.17.10888495592818845871887...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>267</td>\n      <td>9.08 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:34:45.372</td>\n    </tr>\n    <tr>\n      <th>1327</th>\n      <td>1.3.6.1.4.1.9328.50.17.29822011685214407434477...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>PET FDG NAC</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>267</td>\n      <td>9.07 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:43:41.948</td>\n    </tr>\n  </tbody>\n</table>\n<p>1328 rows × 17 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 148
    }
   ],
   "source": [
    "metadata = pd.read_csv(DIR_PATH + '/metadata.csv')\n",
    "metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sorting\n",
    "metadata = metadata.sort_values(by=['Subject ID', 'Study UID'])"
   ]
  },
  {
   "source": [
    "### 2) Check PET-CT Pair Datas"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def checkPETandCT(df):\n",
    "    output = []\n",
    "    for idx in df.groupby(['Subject ID', 'Study UID']).count()['Modality'].index:\n",
    "        value = (df[['Subject ID', 'Study UID']] == idx).values\n",
    "        grid = (value[:,0] == value[:,1]) == (value[:,0] == True)\n",
    "        mods = df.loc[grid, :]['Modality'].values\n",
    "        bools = 'CT' in mods and 'PT' in mods\n",
    "        for loc in df.loc[grid, :]['File Location']:\n",
    "            output.append(bools)\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "1328\n"
     ]
    }
   ],
   "source": [
    "check_petct = checkPETandCT(metadata)\n",
    "print (len(check_petct))"
   ]
  },
  {
   "source": [
    "### 3) Delete Other Datas"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "def delete_dir(df, check, DIR_PATH):\n",
    "    for i, bools in enumerate(check, 0):\n",
    "        if not bools:\n",
    "            ABS_PATH = DIR_PATH + df['File Location'][i][1:]\n",
    "            META_PATH = os.path.join(ABS_PATH, os.pardir)\n",
    "            try:\n",
    "                shutil.rmtree(ABS_PATH)\n",
    "            except: pass\n",
    "            try: os.rmdir(META_PATH)\n",
    "            except: pass\n",
    "            try: os.rmdir(os.path.join(META_PATH, os.pardir))\n",
    "            except: pass\n",
    "    print('Delete Checked Directories Clearly.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Delete Checked Directories Clearly.\n"
     ]
    }
   ],
   "source": [
    "delete_dir(metadata, check_petct, DIR_PATH)"
   ]
  },
  {
   "source": [
    "### 4) Save Preprocessed Metadata"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_metadata = metadata[check_petct]\n",
    "new_metadata.to_csv(DIR_PATH + '/new_metadata.csv', na_rep='null')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Patient Number\nMetadata: 93\nFolder  : 93\n\nSeries Number\nMetadata: 828\nFolder  : 828\n"
     ]
    }
   ],
   "source": [
    "# Validation\n",
    "def count_folder(DIR_PATH):\n",
    "    count = 0\n",
    "    for PATH_1 in os.listdir(DIR_PATH + '/RIDER Lung PET-CT')[1:]:\n",
    "        for PATH_2 in os.listdir(DIR_PATH + '/RIDER Lung PET-CT/'+ PATH_1):\n",
    "            count += len(os.listdir(DIR_PATH + '/RIDER Lung PET-CT/'+ PATH_1+'/'+PATH_2))\n",
    "    return count\n",
    "\n",
    "print (\"Patient Number\")\n",
    "print (\"Metadata:\", len(new_metadata.groupby(['Subject ID']).size().index))\n",
    "print (\"Folder  :\", len(os.listdir(DIR_PATH + '/RIDER Lung PET-CT'))-1)\n",
    "print (\"\")\n",
    "print (\"Series Number\")\n",
    "print (\"Metadata:\", len(new_metadata))\n",
    "print (\"Folder  :\", count_folder(DIR_PATH))"
   ]
  },
  {
   "source": [
    "## 2. Data Selection"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 1) Delete Strange Data\n",
    "#### Procedure\n",
    "1. Make dataframe with possible cases.\n",
    "2. Check data with 3D Slicer Program.\n",
    "3. Delete strange datas."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load new_metadata if needed\n",
    "new_metadata = pd.read_csv(DIR_PATH + '/new_metadata.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     index                                         Series UID  \\\n",
       "0        0  1.3.6.1.4.1.9328.50.17.21539635004042802696358...   \n",
       "1        1  1.3.6.1.4.1.9328.50.17.24674249575175155363249...   \n",
       "2        2  1.3.6.1.4.1.9328.50.17.14298831690972470748724...   \n",
       "3        3  1.3.6.1.4.1.9328.50.17.33448411630762243528802...   \n",
       "4       10  1.3.6.1.4.1.9328.50.17.10979049634014230470074...   \n",
       "..     ...                                                ...   \n",
       "823   1323  1.3.6.1.4.1.9328.50.17.29526182708906873966519...   \n",
       "824   1324  1.3.6.1.4.1.9328.50.17.27647997212178938595947...   \n",
       "825   1325  1.3.6.1.4.1.9328.50.17.29196707572056236162490...   \n",
       "826   1326  1.3.6.1.4.1.9328.50.17.10888495592818845871887...   \n",
       "827   1327  1.3.6.1.4.1.9328.50.17.29822011685214407434477...   \n",
       "\n",
       "            Collection  3rd Party Analysis  \\\n",
       "0    RIDER Lung PET-CT                 NaN   \n",
       "1    RIDER Lung PET-CT                 NaN   \n",
       "2    RIDER Lung PET-CT                 NaN   \n",
       "3    RIDER Lung PET-CT                 NaN   \n",
       "4    RIDER Lung PET-CT                 NaN   \n",
       "..                 ...                 ...   \n",
       "823  RIDER Lung PET-CT                 NaN   \n",
       "824  RIDER Lung PET-CT                 NaN   \n",
       "825  RIDER Lung PET-CT                 NaN   \n",
       "826  RIDER Lung PET-CT                 NaN   \n",
       "827  RIDER Lung PET-CT                 NaN   \n",
       "\n",
       "                              Data Description URI        Subject ID  \\\n",
       "0    https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM  RIDER-1002189179   \n",
       "1    https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM  RIDER-1002189179   \n",
       "2    https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM  RIDER-1002189179   \n",
       "3    https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM  RIDER-1002189179   \n",
       "4    https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM  RIDER-1009463841   \n",
       "..                                             ...               ...   \n",
       "823  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM  RIDER-9640765801   \n",
       "824  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM  RIDER-9640765801   \n",
       "825  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM  RIDER-9640765801   \n",
       "826  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM  RIDER-9640765801   \n",
       "827  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM  RIDER-9640765801   \n",
       "\n",
       "                                             Study UID Study Description  \\\n",
       "0    1.3.6.1.4.1.9328.50.17.74928344181377977351227...               NaN   \n",
       "1    1.3.6.1.4.1.9328.50.17.74928344181377977351227...               NaN   \n",
       "2    1.3.6.1.4.1.9328.50.17.74928344181377977351227...               NaN   \n",
       "3    1.3.6.1.4.1.9328.50.17.74928344181377977351227...               NaN   \n",
       "4    1.3.6.1.4.1.9328.50.17.17116950301501248436577...               NaN   \n",
       "..                                                 ...               ...   \n",
       "823  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   FDG 6AFOV TORSO   \n",
       "824  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   FDG 6AFOV TORSO   \n",
       "825  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   FDG 6AFOV TORSO   \n",
       "826  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   FDG 6AFOV TORSO   \n",
       "827  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   FDG 6AFOV TORSO   \n",
       "\n",
       "     Study Date               Series Description        Manufacturer Modality  \\\n",
       "0    05-07-2006                    CT 2.5mm cb03  GE MEDICAL SYSTEMS       CT   \n",
       "1    05-07-2006                  3D FBP H10 cb03  GE MEDICAL SYSTEMS       PT   \n",
       "2    05-07-2006  5min 3D OSEM 8it28sub 10MM cb03  GE MEDICAL SYSTEMS       PT   \n",
       "3    05-07-2006   5min 3D OSEM 8it28sub 7MM cb03  GE MEDICAL SYSTEMS       PT   \n",
       "4    05-07-2006                    CT 2.5mm cb02  GE MEDICAL SYSTEMS       CT   \n",
       "..          ...                              ...                 ...      ...   \n",
       "823  02-02-2006                 CT SCOUT HEAD IN  GE MEDICAL SYSTEMS       CT   \n",
       "824  02-02-2006                       AC for PET  GE MEDICAL SYSTEMS       CT   \n",
       "825  02-02-2006           70CM FOV 3.27MM CT STD  GE MEDICAL SYSTEMS       CT   \n",
       "826  02-02-2006                      PET FDG SUV  GE MEDICAL SYSTEMS       PT   \n",
       "827  02-02-2006                      PET FDG NAC  GE MEDICAL SYSTEMS       PT   \n",
       "\n",
       "                                 SOP Class Name                SOP Class UID  \\\n",
       "0                              CT Image Storage    1.2.840.10008.5.1.4.1.1.2   \n",
       "1    Positron Emission Tomography Image Storage  1.2.840.10008.5.1.4.1.1.128   \n",
       "2    Positron Emission Tomography Image Storage  1.2.840.10008.5.1.4.1.1.128   \n",
       "3    Positron Emission Tomography Image Storage  1.2.840.10008.5.1.4.1.1.128   \n",
       "4                              CT Image Storage    1.2.840.10008.5.1.4.1.1.2   \n",
       "..                                          ...                          ...   \n",
       "823                            CT Image Storage    1.2.840.10008.5.1.4.1.1.2   \n",
       "824                            CT Image Storage    1.2.840.10008.5.1.4.1.1.2   \n",
       "825                            CT Image Storage    1.2.840.10008.5.1.4.1.1.2   \n",
       "826  Positron Emission Tomography Image Storage  1.2.840.10008.5.1.4.1.1.128   \n",
       "827  Positron Emission Tomography Image Storage  1.2.840.10008.5.1.4.1.1.128   \n",
       "\n",
       "     Number of Images  File Size  \\\n",
       "0                  64   32.12 MB   \n",
       "1                  47    1.59 MB   \n",
       "2                  47    1.59 MB   \n",
       "3                  47    1.59 MB   \n",
       "4                  65   32.62 MB   \n",
       "..                ...        ...   \n",
       "823                 2    4.97 MB   \n",
       "824               429  215.30 MB   \n",
       "825               739  370.88 MB   \n",
       "826               267    9.08 MB   \n",
       "827               267    9.07 MB   \n",
       "\n",
       "                                         File Location  \\\n",
       "0    .\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...   \n",
       "1    .\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...   \n",
       "2    .\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...   \n",
       "3    .\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...   \n",
       "4    .\\RIDER Lung PET-CT\\RIDER-1009463841\\05-07-200...   \n",
       "..                                                 ...   \n",
       "823  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "824  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "825  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "826  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "827  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "\n",
       "          Download Timestamp  \n",
       "0    2021-07-07T21:33:13.717  \n",
       "1    2021-07-07T21:31:39.064  \n",
       "2    2021-07-07T21:31:39.064  \n",
       "3    2021-07-07T21:31:45.154  \n",
       "4     2021-07-07T21:40:08.64  \n",
       "..                       ...  \n",
       "823  2021-07-08T19:43:11.042  \n",
       "824  2021-07-08T19:45:15.912  \n",
       "825   2021-07-08T19:56:04.11  \n",
       "826  2021-07-08T19:34:45.372  \n",
       "827  2021-07-08T19:43:41.948  \n",
       "\n",
       "[828 rows x 18 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>index</th>\n      <th>Series UID</th>\n      <th>Collection</th>\n      <th>3rd Party Analysis</th>\n      <th>Data Description URI</th>\n      <th>Subject ID</th>\n      <th>Study UID</th>\n      <th>Study Description</th>\n      <th>Study Date</th>\n      <th>Series Description</th>\n      <th>Manufacturer</th>\n      <th>Modality</th>\n      <th>SOP Class Name</th>\n      <th>SOP Class UID</th>\n      <th>Number of Images</th>\n      <th>File Size</th>\n      <th>File Location</th>\n      <th>Download Timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1.3.6.1.4.1.9328.50.17.21539635004042802696358...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1002189179</td>\n      <td>1.3.6.1.4.1.9328.50.17.74928344181377977351227...</td>\n      <td>NaN</td>\n      <td>05-07-2006</td>\n      <td>CT 2.5mm cb03</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>64</td>\n      <td>32.12 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...</td>\n      <td>2021-07-07T21:33:13.717</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1.3.6.1.4.1.9328.50.17.24674249575175155363249...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1002189179</td>\n      <td>1.3.6.1.4.1.9328.50.17.74928344181377977351227...</td>\n      <td>NaN</td>\n      <td>05-07-2006</td>\n      <td>3D FBP H10 cb03</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>47</td>\n      <td>1.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...</td>\n      <td>2021-07-07T21:31:39.064</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>1.3.6.1.4.1.9328.50.17.14298831690972470748724...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1002189179</td>\n      <td>1.3.6.1.4.1.9328.50.17.74928344181377977351227...</td>\n      <td>NaN</td>\n      <td>05-07-2006</td>\n      <td>5min 3D OSEM 8it28sub 10MM cb03</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>47</td>\n      <td>1.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...</td>\n      <td>2021-07-07T21:31:39.064</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>1.3.6.1.4.1.9328.50.17.33448411630762243528802...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1002189179</td>\n      <td>1.3.6.1.4.1.9328.50.17.74928344181377977351227...</td>\n      <td>NaN</td>\n      <td>05-07-2006</td>\n      <td>5min 3D OSEM 8it28sub 7MM cb03</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>47</td>\n      <td>1.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...</td>\n      <td>2021-07-07T21:31:45.154</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10</td>\n      <td>1.3.6.1.4.1.9328.50.17.10979049634014230470074...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1009463841</td>\n      <td>1.3.6.1.4.1.9328.50.17.17116950301501248436577...</td>\n      <td>NaN</td>\n      <td>05-07-2006</td>\n      <td>CT 2.5mm cb02</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>65</td>\n      <td>32.62 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1009463841\\05-07-200...</td>\n      <td>2021-07-07T21:40:08.64</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>823</th>\n      <td>1323</td>\n      <td>1.3.6.1.4.1.9328.50.17.29526182708906873966519...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>CT SCOUT HEAD IN</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>2</td>\n      <td>4.97 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:43:11.042</td>\n    </tr>\n    <tr>\n      <th>824</th>\n      <td>1324</td>\n      <td>1.3.6.1.4.1.9328.50.17.27647997212178938595947...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>AC for PET</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>429</td>\n      <td>215.30 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:45:15.912</td>\n    </tr>\n    <tr>\n      <th>825</th>\n      <td>1325</td>\n      <td>1.3.6.1.4.1.9328.50.17.29196707572056236162490...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>70CM FOV 3.27MM CT STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>739</td>\n      <td>370.88 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:56:04.11</td>\n    </tr>\n    <tr>\n      <th>826</th>\n      <td>1326</td>\n      <td>1.3.6.1.4.1.9328.50.17.10888495592818845871887...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>267</td>\n      <td>9.08 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:34:45.372</td>\n    </tr>\n    <tr>\n      <th>827</th>\n      <td>1327</td>\n      <td>1.3.6.1.4.1.9328.50.17.29822011685214407434477...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>PET FDG NAC</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>267</td>\n      <td>9.07 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:43:41.948</td>\n    </tr>\n  </tbody>\n</table>\n<p>828 rows × 18 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 263
    }
   ],
   "source": [
    "sel_metadata = dict()\n",
    "sel_metadata['First'] = new_metadata.reset_index()\n",
    "sel_metadata['First']"
   ]
  },
  {
   "source": [
    "#### a. slice < 10"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            Subject ID    Study Description  Study Date Series Description  \\\n",
       "24    RIDER-1080629396                  NaN  05-23-2006   CT SCOUT HEAD IN   \n",
       "59    RIDER-1172912404      FDG 5AFOV TORSO  12-15-2005   CT SCOUT HEAD IN   \n",
       "66    RIDER-1172912404      FDG 5AFOV TORSO  06-14-2005   CT SCOUT HEAD IN   \n",
       "89    RIDER-1284094278     FDG 7 AFOV TORSO  11-24-2006   CT SCOUT HEAD IN   \n",
       "109   RIDER-1301567537  PET CT LIMITED AREA  01-23-2007     CT SCOUT BRAIN   \n",
       "...                ...                  ...         ...                ...   \n",
       "1257  RIDER-7506348166      FDG 5AFOV TORSO  01-04-2007   CT SCOUT HEAD IN   \n",
       "1278  RIDER-8054797922       FDG 5AFOV LUNG  03-21-2006   CT SCOUT HEAD IN   \n",
       "1288  RIDER-8054797922      FDG 5AFOV TORSO  01-31-2006   CT SCOUT HEAD IN   \n",
       "1306  RIDER-8876965844   2-FOV uniform.test  09-28-2006  CT SCOUT HEAD OUT   \n",
       "1323  RIDER-9640765801      FDG 6AFOV TORSO  02-02-2006   CT SCOUT HEAD IN   \n",
       "\n",
       "     Modality  Number of Images  \\\n",
       "24         CT                 1   \n",
       "59         CT                 2   \n",
       "66         CT                 1   \n",
       "89         CT                 1   \n",
       "109        CT                 1   \n",
       "...       ...               ...   \n",
       "1257       CT                 2   \n",
       "1278       CT                 2   \n",
       "1288       CT                 2   \n",
       "1306       CT                 1   \n",
       "1323       CT                 2   \n",
       "\n",
       "                                          File Location  \n",
       "24    .\\RIDER Lung PET-CT\\RIDER-1080629396\\05-23-200...  \n",
       "59    .\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...  \n",
       "66    .\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...  \n",
       "89    .\\RIDER Lung PET-CT\\RIDER-1284094278\\11-24-200...  \n",
       "109   .\\RIDER Lung PET-CT\\RIDER-1301567537\\01-23-200...  \n",
       "...                                                 ...  \n",
       "1257  .\\RIDER Lung PET-CT\\RIDER-7506348166\\01-04-200...  \n",
       "1278  .\\RIDER Lung PET-CT\\RIDER-8054797922\\03-21-200...  \n",
       "1288  .\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...  \n",
       "1306  .\\RIDER Lung PET-CT\\RIDER-8876965844\\09-28-200...  \n",
       "1323  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...  \n",
       "\n",
       "[69 rows x 7 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Subject ID</th>\n      <th>Study Description</th>\n      <th>Study Date</th>\n      <th>Series Description</th>\n      <th>Modality</th>\n      <th>Number of Images</th>\n      <th>File Location</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>24</th>\n      <td>RIDER-1080629396</td>\n      <td>NaN</td>\n      <td>05-23-2006</td>\n      <td>CT SCOUT HEAD IN</td>\n      <td>CT</td>\n      <td>1</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1080629396\\05-23-200...</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>RIDER-1172912404</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>12-15-2005</td>\n      <td>CT SCOUT HEAD IN</td>\n      <td>CT</td>\n      <td>2</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>RIDER-1172912404</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-14-2005</td>\n      <td>CT SCOUT HEAD IN</td>\n      <td>CT</td>\n      <td>1</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>RIDER-1284094278</td>\n      <td>FDG 7 AFOV TORSO</td>\n      <td>11-24-2006</td>\n      <td>CT SCOUT HEAD IN</td>\n      <td>CT</td>\n      <td>1</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1284094278\\11-24-200...</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>RIDER-1301567537</td>\n      <td>PET CT LIMITED AREA</td>\n      <td>01-23-2007</td>\n      <td>CT SCOUT BRAIN</td>\n      <td>CT</td>\n      <td>1</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1301567537\\01-23-200...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1257</th>\n      <td>RIDER-7506348166</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>01-04-2007</td>\n      <td>CT SCOUT HEAD IN</td>\n      <td>CT</td>\n      <td>2</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-7506348166\\01-04-200...</td>\n    </tr>\n    <tr>\n      <th>1278</th>\n      <td>RIDER-8054797922</td>\n      <td>FDG 5AFOV LUNG</td>\n      <td>03-21-2006</td>\n      <td>CT SCOUT HEAD IN</td>\n      <td>CT</td>\n      <td>2</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\03-21-200...</td>\n    </tr>\n    <tr>\n      <th>1288</th>\n      <td>RIDER-8054797922</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>01-31-2006</td>\n      <td>CT SCOUT HEAD IN</td>\n      <td>CT</td>\n      <td>2</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...</td>\n    </tr>\n    <tr>\n      <th>1306</th>\n      <td>RIDER-8876965844</td>\n      <td>2-FOV uniform.test</td>\n      <td>09-28-2006</td>\n      <td>CT SCOUT HEAD OUT</td>\n      <td>CT</td>\n      <td>1</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8876965844\\09-28-200...</td>\n    </tr>\n    <tr>\n      <th>1323</th>\n      <td>RIDER-9640765801</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>CT SCOUT HEAD IN</td>\n      <td>CT</td>\n      <td>2</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n    </tr>\n  </tbody>\n</table>\n<p>69 rows × 7 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 223
    }
   ],
   "source": [
    "# Check Data with (# of .dcm) < 10\n",
    "min_df = new_metadata.loc[new_metadata['Number of Images']<10, ['Subject ID', 'Study Description', 'Study Date', 'Series Description', 'Modality', 'Number of Images', 'File Location']]\n",
    "min_df.to_csv(DIR_PATH + '/min_df.csv', na_rep='null')\n",
    "min_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy data to './min_df_data'\n",
    "for loc in min_df['File Location']:\n",
    "    shutil.copytree(DIR_PATH + loc[1:], DIR_PATH + '/min_df_data' + loc[1:])"
   ]
  },
  {
   "source": [
    "print('Check data with 3D Slicer Program...')\n",
    "print('Conclusion : No Useful Data, Delete All')"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 237,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Check data with 3D Slicer Program...\nConclusion : No Useful Data, Delete All\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete All\n",
    "for loc in min_df['File Location']:\n",
    "    ABS_PATH = DIR_PATH + loc[1:]\n",
    "    UPPER_PATH = os.path.join(ABS_PATH, os.pardir)\n",
    "    try: shutil.rmtree(ABS_PATH)\n",
    "    except: pass\n",
    "    try: os.rmdir(UPPER_PATH)\n",
    "    except: pass\n",
    "    try: os.rmdir(os.path.join(UPPER_PATH, os.pardir))\n",
    "    except: pass\n",
    "\n",
    "# Make New DataFrame\n",
    "sel_loc = []\n",
    "for loc in sel_metadata['First']['index']:\n",
    "    if loc in min_df.index: sel_loc.append(False)\n",
    "    else: sel_loc.append(True)\n",
    "sel_metadata['Min'] = sel_metadata['First'].iloc[sel_loc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Patient Number\nMetadata: 93\nFolder  : 93\n\nSeries Number\nMetadata: 759\nFolder  : 759\n"
     ]
    }
   ],
   "source": [
    "# Validation\n",
    "\n",
    "print (\"Patient Number\")\n",
    "print (\"Metadata:\", len(sel_metadata['Min'].groupby(['Subject ID']).size().index))\n",
    "print (\"Folder  :\", len(os.listdir(DIR_PATH + '/RIDER Lung PET-CT'))-1)\n",
    "print (\"\")\n",
    "print (\"Series Number\")\n",
    "print (\"Metadata:\", len(sel_metadata['Min']))\n",
    "print (\"Folder  :\", count_folder(DIR_PATH))"
   ]
  },
  {
   "source": [
    "#### b. 10 <= slice < 100"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           Subject ID   Study Description  Study Date  \\\n",
       "0    RIDER-1002189179                 NaN  05-07-2006   \n",
       "1    RIDER-1002189179                 NaN  05-07-2006   \n",
       "2    RIDER-1002189179                 NaN  05-07-2006   \n",
       "3    RIDER-1002189179                 NaN  05-07-2006   \n",
       "4    RIDER-1009463841                 NaN  05-07-2006   \n",
       "..                ...                 ...         ...   \n",
       "816  RIDER-8876965844  2-FOV uniform.test  09-28-2006   \n",
       "817  RIDER-8876965844  2-FOV uniform.test  09-28-2006   \n",
       "818  RIDER-8876965844  2-FOV uniform.test  09-28-2006   \n",
       "819  RIDER-8876965844  2-FOV uniform.test  09-28-2006   \n",
       "820  RIDER-8876965844  2-FOV uniform.test  09-28-2006   \n",
       "\n",
       "                     Series Description Modality  Number of Images  \\\n",
       "0                         CT 2.5mm cb03       CT                64   \n",
       "1                       3D FBP H10 cb03       PT                47   \n",
       "2       5min 3D OSEM 8it28sub 10MM cb03       PT                47   \n",
       "3        5min 3D OSEM 8it28sub 7MM cb03       PT                47   \n",
       "4                         CT 2.5mm cb02       CT                65   \n",
       "..                                  ...      ...               ...   \n",
       "816   2AFOV-UniformTest 3D DefaultRecon       PT                91   \n",
       "817                   FDG 3D XAFOV OSEM       PT                91   \n",
       "818  2AFOV-UniformTest 2D ReconDefaults       PT                91   \n",
       "819                         PET FDG SUV       PT                91   \n",
       "820                         PET FDG NAC       PT                91   \n",
       "\n",
       "                                         File Location  \n",
       "0    .\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...  \n",
       "1    .\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...  \n",
       "2    .\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...  \n",
       "3    .\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...  \n",
       "4    .\\RIDER Lung PET-CT\\RIDER-1009463841\\05-07-200...  \n",
       "..                                                 ...  \n",
       "816  .\\RIDER Lung PET-CT\\RIDER-8876965844\\09-28-200...  \n",
       "817  .\\RIDER Lung PET-CT\\RIDER-8876965844\\09-28-200...  \n",
       "818  .\\RIDER Lung PET-CT\\RIDER-8876965844\\09-28-200...  \n",
       "819  .\\RIDER Lung PET-CT\\RIDER-8876965844\\09-28-200...  \n",
       "820  .\\RIDER Lung PET-CT\\RIDER-8876965844\\09-28-200...  \n",
       "\n",
       "[327 rows x 7 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Subject ID</th>\n      <th>Study Description</th>\n      <th>Study Date</th>\n      <th>Series Description</th>\n      <th>Modality</th>\n      <th>Number of Images</th>\n      <th>File Location</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>RIDER-1002189179</td>\n      <td>NaN</td>\n      <td>05-07-2006</td>\n      <td>CT 2.5mm cb03</td>\n      <td>CT</td>\n      <td>64</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>RIDER-1002189179</td>\n      <td>NaN</td>\n      <td>05-07-2006</td>\n      <td>3D FBP H10 cb03</td>\n      <td>PT</td>\n      <td>47</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>RIDER-1002189179</td>\n      <td>NaN</td>\n      <td>05-07-2006</td>\n      <td>5min 3D OSEM 8it28sub 10MM cb03</td>\n      <td>PT</td>\n      <td>47</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>RIDER-1002189179</td>\n      <td>NaN</td>\n      <td>05-07-2006</td>\n      <td>5min 3D OSEM 8it28sub 7MM cb03</td>\n      <td>PT</td>\n      <td>47</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1002189179\\05-07-200...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>RIDER-1009463841</td>\n      <td>NaN</td>\n      <td>05-07-2006</td>\n      <td>CT 2.5mm cb02</td>\n      <td>CT</td>\n      <td>65</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1009463841\\05-07-200...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>816</th>\n      <td>RIDER-8876965844</td>\n      <td>2-FOV uniform.test</td>\n      <td>09-28-2006</td>\n      <td>2AFOV-UniformTest 3D DefaultRecon</td>\n      <td>PT</td>\n      <td>91</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8876965844\\09-28-200...</td>\n    </tr>\n    <tr>\n      <th>817</th>\n      <td>RIDER-8876965844</td>\n      <td>2-FOV uniform.test</td>\n      <td>09-28-2006</td>\n      <td>FDG 3D XAFOV OSEM</td>\n      <td>PT</td>\n      <td>91</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8876965844\\09-28-200...</td>\n    </tr>\n    <tr>\n      <th>818</th>\n      <td>RIDER-8876965844</td>\n      <td>2-FOV uniform.test</td>\n      <td>09-28-2006</td>\n      <td>2AFOV-UniformTest 2D ReconDefaults</td>\n      <td>PT</td>\n      <td>91</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8876965844\\09-28-200...</td>\n    </tr>\n    <tr>\n      <th>819</th>\n      <td>RIDER-8876965844</td>\n      <td>2-FOV uniform.test</td>\n      <td>09-28-2006</td>\n      <td>PET FDG SUV</td>\n      <td>PT</td>\n      <td>91</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8876965844\\09-28-200...</td>\n    </tr>\n    <tr>\n      <th>820</th>\n      <td>RIDER-8876965844</td>\n      <td>2-FOV uniform.test</td>\n      <td>09-28-2006</td>\n      <td>PET FDG NAC</td>\n      <td>PT</td>\n      <td>91</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8876965844\\09-28-200...</td>\n    </tr>\n  </tbody>\n</table>\n<p>327 rows × 7 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "# Check Data with 10<= (# of .dcm) < 100\n",
    "max_df = sel_metadata['Min'].loc[sel_metadata['Min']['Number of Images']<100, ['Subject ID', 'Study Description', 'Study Date', 'Series Description', 'Modality', 'Number of Images', 'File Location']]\n",
    "max_df.to_csv(DIR_PATH + '/max_df.csv', na_rep='null')\n",
    "max_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy data to './max_df_data'\n",
    "for loc in max_df['File Location']:\n",
    "    shutil.copytree(DIR_PATH + loc[1:], DIR_PATH + '/max_df_data' + loc[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Check data with 3D Slicer Program...\nConclusion : Check All Study/Series Description\n"
     ]
    }
   ],
   "source": [
    "print('Check data with 3D Slicer Program...')\n",
    "print('Conclusion : Check All Study/Series Description')"
   ]
  },
  {
   "source": [
    "### 2) Study/Series Description Analysis"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "#### a. Index Analysis"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Study Description\n",
       "2-FOV uniform.test                                                    8\n",
       "2AFOV PELVIS POST CATH                                                2\n",
       "3D decay linearity                                                    5\n",
       "FDG 10 AFOV WB                                                        8\n",
       "FDG 2AFOV PELVIS WCAT                                                 2\n",
       "FDG 5 AFOV TORSO                                                      7\n",
       "FDG 5AFOV LUNG                                                        7\n",
       "FDG 5AFOV SWEEP                                                      11\n",
       "FDG 5AFOV TORSO                                                     200\n",
       "FDG 5FOV TORSO                                                        9\n",
       "FDG 6AFOV TORSO                                                       9\n",
       "FDG 7 AFOV TORSO                                                      7\n",
       "FDG BRIAIN                                                            5\n",
       "FDG5AFOV TORSO                                                        6\n",
       "PET CARDIAC FLOW                                                     33\n",
       "PET CT LIMITED AREA                                                   5\n",
       "PET CT MID BODY                                                     121\n",
       "PET CT WHOLE BODY                                                    21\n",
       "PET TUMOR METAB                                                       6\n",
       "PET TUMOR METABOLISM                                                 36\n",
       "PinSourceImg                                                          5\n",
       "Tomoscintigrafia PET t                                               20\n",
       "Tomoscintigrafia PET total body TOMOSCINTIGRAFIA TOTAL BODY-Tomo      9\n",
       "e-1                                                                   7\n",
       "e1 FDG 6AFOV TORSO                                                    2\n",
       "test CTAC radial trunc                                               30\n",
       "testCTACw35ccContrast                                                10\n",
       "testCTACw75ccContrast                                                10\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "new_metadata.groupby(['Study Description']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Series Description\n",
       "10 bin OSEM noFilter                   1\n",
       "10 binGated OSEMI2S20NF                1\n",
       "10GateFOREFBPHan10.0                   1\n",
       "10GateFOREFBPHan13                     1\n",
       "10GateFOREFBPHan16                     1\n",
       "                                      ..\n",
       "noShiftCorr 3D 8min IQ NEMA shifted    1\n",
       "wCorrCTContr35cc                       1\n",
       "wCorrCTContr75cc                       1\n",
       "wCorrCTcontr35ccCTAC                   1\n",
       "wCorrCTcontr75ccCTAC                   1\n",
       "Length: 339, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "new_metadata.groupby(['Series Description']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Series Description\n",
       "70CM FOV 3.27MM CT STD     47\n",
       "AC for PET                 59\n",
       "AVGR1 CTAC                  3\n",
       "CINECTAC OSEM I2 S20        9\n",
       "CT 2.5MM LUNG              40\n",
       "CT 2.5MM STD               53\n",
       "CT 2.5mm                    8\n",
       "CT SCOUT BRAIN             13\n",
       "CT SCOUT HEAD IN           46\n",
       "CTAC                       11\n",
       "CTAC 2.5 THICK              5\n",
       "CTAC 2.75 thick             3\n",
       "CTAC 3.75 thick            14\n",
       "FDG 3D XAFOV OSEM           3\n",
       "FDG OSEM I2 S20             9\n",
       "Gated 10mm OSEM I2 S20     12\n",
       "HELICAL OSEM I2 S20         7\n",
       "NAC2D                       4\n",
       "OSEM CineCTAC               3\n",
       "Offline Image               3\n",
       "PET FDG NAC                44\n",
       "PET FDG SUV                59\n",
       "Recon 2 CTAC 2.5 THICK      4\n",
       "Recon 2 CTAC 2.75 thick     3\n",
       "Recon 2 CTAC 3.75 thick    12\n",
       "Recon 3 CTAC 2.5 THICK      4\n",
       "Recon 3 CTAC 3.75 thick     7\n",
       "StandardFull               11\n",
       "WB2DVuePoint                4\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "series_df = new_metadata.groupby(['Series Description']).size()\n",
    "series_df[series_df > 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Index Selection by 3D Splicer View Classification\r\n",
    "STUDY_IDX = ['FDG 5AFOV TORSO', 'FDG5AFOV TORSO', 'FDG 5 AFOV TORSO',\r\n",
    "             'FDG 5FOV TORSO', 'FDG 5AFOV LUNG',\r\n",
    "             'FDG 6AFOV TORSO', 'e1 FDG 6AFOV TORSO', 'FDG 7 AFOV TORSO',\r\n",
    "             'PET CT MID BODY', 'PET CT WHOLE BODY',\r\n",
    "             'PET TUMOR METAB', 'PET TUMOR METABOLISM']\r\n",
    "SERIES_IDX = ['CT 2.5MM STD', 'PET FDG SUV']\r\n",
    "CT_ALTER_IDX = ['Recon 2 CTAC 2.5 THICK']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                             Series UID         Collection  \\\n",
       "62    1.3.6.1.4.1.9328.50.17.23500193269190786828912...  RIDER Lung PET-CT   \n",
       "65    1.3.6.1.4.1.9328.50.17.71370567559995220330016...  RIDER Lung PET-CT   \n",
       "69    1.3.6.1.4.1.9328.50.17.28880173671501351185240...  RIDER Lung PET-CT   \n",
       "71    1.3.6.1.4.1.9328.50.17.10308613002188064065304...  RIDER Lung PET-CT   \n",
       "90    1.3.6.1.4.1.9328.50.17.23846650305435481959640...  RIDER Lung PET-CT   \n",
       "...                                                 ...                ...   \n",
       "1287  1.3.6.1.4.1.9328.50.17.19429515156664107209992...  RIDER Lung PET-CT   \n",
       "1290  1.3.6.1.4.1.9328.50.17.23927517269629765042831...  RIDER Lung PET-CT   \n",
       "1293  1.3.6.1.4.1.9328.50.17.16650979549258755223925...  RIDER Lung PET-CT   \n",
       "1322  1.3.6.1.4.1.9328.50.17.11527879251296425726955...  RIDER Lung PET-CT   \n",
       "1326  1.3.6.1.4.1.9328.50.17.10888495592818845871887...  RIDER Lung PET-CT   \n",
       "\n",
       "      3rd Party Analysis                           Data Description URI  \\\n",
       "62                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "65                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "69                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "71                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "90                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "...                  ...                                            ...   \n",
       "1287                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1290                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1293                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1322                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1326                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "\n",
       "            Subject ID                                          Study UID  \\\n",
       "62    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.24892338155574864998303...   \n",
       "65    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.24892338155574864998303...   \n",
       "69    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.29723960320980340557147...   \n",
       "71    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.29723960320980340557147...   \n",
       "90    RIDER-1284094278  1.3.6.1.4.1.9328.50.17.22545924354527867084396...   \n",
       "...                ...                                                ...   \n",
       "1287  RIDER-8054797922  1.3.6.1.4.1.9328.50.17.26884175944205502857742...   \n",
       "1290  RIDER-8054797922  1.3.6.1.4.1.9328.50.17.72443068618977843685376...   \n",
       "1293  RIDER-8054797922  1.3.6.1.4.1.9328.50.17.72443068618977843685376...   \n",
       "1322  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "1326  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "\n",
       "     Study Description  Study Date      Series Description  \\\n",
       "62     FDG 5AFOV TORSO  12-15-2005            CT 2.5MM STD   \n",
       "65     FDG 5AFOV TORSO  12-15-2005             PET FDG SUV   \n",
       "69     FDG 5AFOV TORSO  06-14-2005  Recon 2 CTAC 2.5 THICK   \n",
       "71     FDG 5AFOV TORSO  06-14-2005             PET FDG SUV   \n",
       "90    FDG 7 AFOV TORSO  11-24-2006            CT 2.5MM STD   \n",
       "...                ...         ...                     ...   \n",
       "1287   FDG 5AFOV TORSO  06-02-2005             PET FDG SUV   \n",
       "1290   FDG 5AFOV TORSO  01-31-2006            CT 2.5MM STD   \n",
       "1293   FDG 5AFOV TORSO  01-31-2006             PET FDG SUV   \n",
       "1322   FDG 6AFOV TORSO  02-02-2006            CT 2.5MM STD   \n",
       "1326   FDG 6AFOV TORSO  02-02-2006             PET FDG SUV   \n",
       "\n",
       "            Manufacturer Modality                              SOP Class Name  \\\n",
       "62    GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "65    GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "69    GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "71    GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "90    GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "...                  ...      ...                                         ...   \n",
       "1287  GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "1290  GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "1293  GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "1322  GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "1326  GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "\n",
       "                    SOP Class UID  Number of Images  File Size  \\\n",
       "62      1.2.840.10008.5.1.4.1.1.2               290  145.55 MB   \n",
       "65    1.2.840.10008.5.1.4.1.1.128               223    7.58 MB   \n",
       "69      1.2.840.10008.5.1.4.1.1.2               291  146.03 MB   \n",
       "71    1.2.840.10008.5.1.4.1.1.128               223    7.59 MB   \n",
       "90      1.2.840.10008.5.1.4.1.1.2               358  179.67 MB   \n",
       "...                           ...               ...        ...   \n",
       "1287  1.2.840.10008.5.1.4.1.1.128               223    7.59 MB   \n",
       "1290    1.2.840.10008.5.1.4.1.1.2               295  148.06 MB   \n",
       "1293  1.2.840.10008.5.1.4.1.1.128               223    7.59 MB   \n",
       "1322    1.2.840.10008.5.1.4.1.1.2               355  178.18 MB   \n",
       "1326  1.2.840.10008.5.1.4.1.1.128               267    9.08 MB   \n",
       "\n",
       "                                          File Location  \\\n",
       "62    .\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...   \n",
       "65    .\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...   \n",
       "69    .\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...   \n",
       "71    .\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...   \n",
       "90    .\\RIDER Lung PET-CT\\RIDER-1284094278\\11-24-200...   \n",
       "...                                                 ...   \n",
       "1287  .\\RIDER Lung PET-CT\\RIDER-8054797922\\06-02-200...   \n",
       "1290  .\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...   \n",
       "1293  .\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...   \n",
       "1322  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "1326  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "\n",
       "           Download Timestamp  \n",
       "62     2021-07-07T22:18:24.46  \n",
       "65    2021-07-07T22:19:49.764  \n",
       "69    2021-07-07T22:30:22.011  \n",
       "71    2021-07-07T22:20:57.592  \n",
       "90    2021-07-07T22:50:03.194  \n",
       "...                       ...  \n",
       "1287  2021-07-08T18:42:24.587  \n",
       "1290  2021-07-08T19:05:28.415  \n",
       "1293  2021-07-08T18:51:02.864  \n",
       "1322  2021-07-08T19:42:54.418  \n",
       "1326  2021-07-08T19:34:45.372  \n",
       "\n",
       "[110 rows x 17 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Series UID</th>\n      <th>Collection</th>\n      <th>3rd Party Analysis</th>\n      <th>Data Description URI</th>\n      <th>Subject ID</th>\n      <th>Study UID</th>\n      <th>Study Description</th>\n      <th>Study Date</th>\n      <th>Series Description</th>\n      <th>Manufacturer</th>\n      <th>Modality</th>\n      <th>SOP Class Name</th>\n      <th>SOP Class UID</th>\n      <th>Number of Images</th>\n      <th>File Size</th>\n      <th>File Location</th>\n      <th>Download Timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>62</th>\n      <td>1.3.6.1.4.1.9328.50.17.23500193269190786828912...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.24892338155574864998303...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>12-15-2005</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>290</td>\n      <td>145.55 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...</td>\n      <td>2021-07-07T22:18:24.46</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>1.3.6.1.4.1.9328.50.17.71370567559995220330016...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.24892338155574864998303...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>12-15-2005</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.58 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...</td>\n      <td>2021-07-07T22:19:49.764</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>1.3.6.1.4.1.9328.50.17.28880173671501351185240...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.29723960320980340557147...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-14-2005</td>\n      <td>Recon 2 CTAC 2.5 THICK</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>291</td>\n      <td>146.03 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...</td>\n      <td>2021-07-07T22:30:22.011</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>1.3.6.1.4.1.9328.50.17.10308613002188064065304...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.29723960320980340557147...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-14-2005</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...</td>\n      <td>2021-07-07T22:20:57.592</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>1.3.6.1.4.1.9328.50.17.23846650305435481959640...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1284094278</td>\n      <td>1.3.6.1.4.1.9328.50.17.22545924354527867084396...</td>\n      <td>FDG 7 AFOV TORSO</td>\n      <td>11-24-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>358</td>\n      <td>179.67 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1284094278\\11-24-200...</td>\n      <td>2021-07-07T22:50:03.194</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1287</th>\n      <td>1.3.6.1.4.1.9328.50.17.19429515156664107209992...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-8054797922</td>\n      <td>1.3.6.1.4.1.9328.50.17.26884175944205502857742...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-02-2005</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\06-02-200...</td>\n      <td>2021-07-08T18:42:24.587</td>\n    </tr>\n    <tr>\n      <th>1290</th>\n      <td>1.3.6.1.4.1.9328.50.17.23927517269629765042831...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-8054797922</td>\n      <td>1.3.6.1.4.1.9328.50.17.72443068618977843685376...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>01-31-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>295</td>\n      <td>148.06 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...</td>\n      <td>2021-07-08T19:05:28.415</td>\n    </tr>\n    <tr>\n      <th>1293</th>\n      <td>1.3.6.1.4.1.9328.50.17.16650979549258755223925...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-8054797922</td>\n      <td>1.3.6.1.4.1.9328.50.17.72443068618977843685376...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>01-31-2006</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...</td>\n      <td>2021-07-08T18:51:02.864</td>\n    </tr>\n    <tr>\n      <th>1322</th>\n      <td>1.3.6.1.4.1.9328.50.17.11527879251296425726955...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>355</td>\n      <td>178.18 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:42:54.418</td>\n    </tr>\n    <tr>\n      <th>1326</th>\n      <td>1.3.6.1.4.1.9328.50.17.10888495592818845871887...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>267</td>\n      <td>9.08 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:34:45.372</td>\n    </tr>\n  </tbody>\n</table>\n<p>110 rows × 17 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "study_bool = (new_metadata['Study Description'].isin(STUDY_IDX))\n",
    "series_bool = (new_metadata['Series Description'].isin(SERIES_IDX))\n",
    "alter_bool = (new_metadata['Series Description'].isin(CT_ALTER_IDX))\n",
    "new_metadata[study_bool & (series_bool|alter_bool)]"
   ]
  },
  {
   "source": [
    "#### b. Pair Check"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Subject ID        Study UID                                                     \n",
       "RIDER-2617411955  1.3.6.1.4.1.9328.50.17.136661214566929734487449468532449408407    3\n",
       "RIDER-2736200846  1.3.6.1.4.1.9328.50.17.321263577298382633781490600655417749542    3\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 73
    }
   ],
   "source": [
    "# Validation\n",
    "pair_metadata = new_metadata[study_bool & (series_bool|alter_bool)]\n",
    "pair_size = pair_metadata.groupby(['Subject ID', 'Study UID']).size()\n",
    "pair_size[pair_size!=2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Subject ID        Study UID                                                       Modality\n",
       "RIDER-2617411955  1.3.6.1.4.1.9328.50.17.136661214566929734487449468532449408407  PT          2\n",
       "RIDER-2736200846  1.3.6.1.4.1.9328.50.17.321263577298382633781490600655417749542  PT          2\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 54
    }
   ],
   "source": [
    "# Check Doublet Modality\r\n",
    "mode_size = pair_metadata.groupby(['Subject ID', 'Study UID', 'Modality']).size()\r\n",
    "mode_size[mode_size!=1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "           Subject ID  Study Description Series Description  Number of Images\n761  RIDER-2617411955  PET CT WHOLE BODY       CT 2.5MM STD               303\n771  RIDER-2617411955  PET CT WHOLE BODY        PET FDG SUV               223\n774  RIDER-2617411955  PET CT WHOLE BODY        PET FDG SUV               267\n           Subject ID Study Description Series Description  Number of Images\n787  RIDER-2736200846   FDG 5AFOV TORSO       CT 2.5MM STD               338\n795  RIDER-2736200846   FDG 5AFOV TORSO        PET FDG SUV               135\n798  RIDER-2736200846   FDG 5AFOV TORSO        PET FDG SUV               267\n"
     ]
    }
   ],
   "source": [
    "# Check Triplet Data\n",
    "for subject, study in pair_size[pair_size!=2].index:\n",
    "    print(pair_metadata[(pair_metadata['Subject ID']==subject) & (pair_metadata['Study UID']==study)][['Subject ID', 'Study Description', 'Series Description', 'Number of Images']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                             Series UID         Collection  \\\n",
       "62    1.3.6.1.4.1.9328.50.17.23500193269190786828912...  RIDER Lung PET-CT   \n",
       "65    1.3.6.1.4.1.9328.50.17.71370567559995220330016...  RIDER Lung PET-CT   \n",
       "69    1.3.6.1.4.1.9328.50.17.28880173671501351185240...  RIDER Lung PET-CT   \n",
       "71    1.3.6.1.4.1.9328.50.17.10308613002188064065304...  RIDER Lung PET-CT   \n",
       "90    1.3.6.1.4.1.9328.50.17.23846650305435481959640...  RIDER Lung PET-CT   \n",
       "...                                                 ...                ...   \n",
       "1287  1.3.6.1.4.1.9328.50.17.19429515156664107209992...  RIDER Lung PET-CT   \n",
       "1290  1.3.6.1.4.1.9328.50.17.23927517269629765042831...  RIDER Lung PET-CT   \n",
       "1293  1.3.6.1.4.1.9328.50.17.16650979549258755223925...  RIDER Lung PET-CT   \n",
       "1322  1.3.6.1.4.1.9328.50.17.11527879251296425726955...  RIDER Lung PET-CT   \n",
       "1326  1.3.6.1.4.1.9328.50.17.10888495592818845871887...  RIDER Lung PET-CT   \n",
       "\n",
       "      3rd Party Analysis                           Data Description URI  \\\n",
       "62                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "65                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "69                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "71                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "90                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "...                  ...                                            ...   \n",
       "1287                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1290                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1293                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1322                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1326                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "\n",
       "            Subject ID                                          Study UID  \\\n",
       "62    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.24892338155574864998303...   \n",
       "65    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.24892338155574864998303...   \n",
       "69    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.29723960320980340557147...   \n",
       "71    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.29723960320980340557147...   \n",
       "90    RIDER-1284094278  1.3.6.1.4.1.9328.50.17.22545924354527867084396...   \n",
       "...                ...                                                ...   \n",
       "1287  RIDER-8054797922  1.3.6.1.4.1.9328.50.17.26884175944205502857742...   \n",
       "1290  RIDER-8054797922  1.3.6.1.4.1.9328.50.17.72443068618977843685376...   \n",
       "1293  RIDER-8054797922  1.3.6.1.4.1.9328.50.17.72443068618977843685376...   \n",
       "1322  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "1326  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "\n",
       "     Study Description  Study Date      Series Description  \\\n",
       "62     FDG 5AFOV TORSO  12-15-2005            CT 2.5MM STD   \n",
       "65     FDG 5AFOV TORSO  12-15-2005             PET FDG SUV   \n",
       "69     FDG 5AFOV TORSO  06-14-2005  Recon 2 CTAC 2.5 THICK   \n",
       "71     FDG 5AFOV TORSO  06-14-2005             PET FDG SUV   \n",
       "90    FDG 7 AFOV TORSO  11-24-2006            CT 2.5MM STD   \n",
       "...                ...         ...                     ...   \n",
       "1287   FDG 5AFOV TORSO  06-02-2005             PET FDG SUV   \n",
       "1290   FDG 5AFOV TORSO  01-31-2006            CT 2.5MM STD   \n",
       "1293   FDG 5AFOV TORSO  01-31-2006             PET FDG SUV   \n",
       "1322   FDG 6AFOV TORSO  02-02-2006            CT 2.5MM STD   \n",
       "1326   FDG 6AFOV TORSO  02-02-2006             PET FDG SUV   \n",
       "\n",
       "            Manufacturer Modality                              SOP Class Name  \\\n",
       "62    GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "65    GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "69    GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "71    GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "90    GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "...                  ...      ...                                         ...   \n",
       "1287  GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "1290  GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "1293  GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "1322  GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "1326  GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "\n",
       "                    SOP Class UID  Number of Images  File Size  \\\n",
       "62      1.2.840.10008.5.1.4.1.1.2               290  145.55 MB   \n",
       "65    1.2.840.10008.5.1.4.1.1.128               223    7.58 MB   \n",
       "69      1.2.840.10008.5.1.4.1.1.2               291  146.03 MB   \n",
       "71    1.2.840.10008.5.1.4.1.1.128               223    7.59 MB   \n",
       "90      1.2.840.10008.5.1.4.1.1.2               358  179.67 MB   \n",
       "...                           ...               ...        ...   \n",
       "1287  1.2.840.10008.5.1.4.1.1.128               223    7.59 MB   \n",
       "1290    1.2.840.10008.5.1.4.1.1.2               295  148.06 MB   \n",
       "1293  1.2.840.10008.5.1.4.1.1.128               223    7.59 MB   \n",
       "1322    1.2.840.10008.5.1.4.1.1.2               355  178.18 MB   \n",
       "1326  1.2.840.10008.5.1.4.1.1.128               267    9.08 MB   \n",
       "\n",
       "                                          File Location  \\\n",
       "62    .\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...   \n",
       "65    .\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...   \n",
       "69    .\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...   \n",
       "71    .\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...   \n",
       "90    .\\RIDER Lung PET-CT\\RIDER-1284094278\\11-24-200...   \n",
       "...                                                 ...   \n",
       "1287  .\\RIDER Lung PET-CT\\RIDER-8054797922\\06-02-200...   \n",
       "1290  .\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...   \n",
       "1293  .\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...   \n",
       "1322  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "1326  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "\n",
       "           Download Timestamp  \n",
       "62     2021-07-07T22:18:24.46  \n",
       "65    2021-07-07T22:19:49.764  \n",
       "69    2021-07-07T22:30:22.011  \n",
       "71    2021-07-07T22:20:57.592  \n",
       "90    2021-07-07T22:50:03.194  \n",
       "...                       ...  \n",
       "1287  2021-07-08T18:42:24.587  \n",
       "1290  2021-07-08T19:05:28.415  \n",
       "1293  2021-07-08T18:51:02.864  \n",
       "1322  2021-07-08T19:42:54.418  \n",
       "1326  2021-07-08T19:34:45.372  \n",
       "\n",
       "[108 rows x 17 columns]"
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<td>1.3.6.1.4.1.9328.50.17.23500193269190786828912...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.24892338155574864998303...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>12-15-2005</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>290</td>\n      <td>145.55 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...</td>\n      <td>2021-07-07T22:18:24.46</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>1.3.6.1.4.1.9328.50.17.71370567559995220330016...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.24892338155574864998303...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>12-15-2005</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.58 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...</td>\n      <td>2021-07-07T22:19:49.764</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>1.3.6.1.4.1.9328.50.17.28880173671501351185240...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.29723960320980340557147...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-14-2005</td>\n      <td>Recon 2 CTAC 2.5 THICK</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>291</td>\n      <td>146.03 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...</td>\n      <td>2021-07-07T22:30:22.011</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>1.3.6.1.4.1.9328.50.17.10308613002188064065304...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.29723960320980340557147...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-14-2005</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...</td>\n      <td>2021-07-07T22:20:57.592</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>1.3.6.1.4.1.9328.50.17.23846650305435481959640...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1284094278</td>\n      <td>1.3.6.1.4.1.9328.50.17.22545924354527867084396...</td>\n      <td>FDG 7 AFOV TORSO</td>\n      <td>11-24-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>358</td>\n      <td>179.67 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1284094278\\11-24-200...</td>\n      <td>2021-07-07T22:50:03.194</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1287</th>\n      <td>1.3.6.1.4.1.9328.50.17.19429515156664107209992...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-8054797922</td>\n      <td>1.3.6.1.4.1.9328.50.17.26884175944205502857742...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-02-2005</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\06-02-200...</td>\n      <td>2021-07-08T18:42:24.587</td>\n    </tr>\n    <tr>\n      <th>1290</th>\n      <td>1.3.6.1.4.1.9328.50.17.23927517269629765042831...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-8054797922</td>\n      <td>1.3.6.1.4.1.9328.50.17.72443068618977843685376...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>01-31-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>295</td>\n      <td>148.06 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...</td>\n      <td>2021-07-08T19:05:28.415</td>\n    </tr>\n    <tr>\n      <th>1293</th>\n      <td>1.3.6.1.4.1.9328.50.17.16650979549258755223925...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-8054797922</td>\n      <td>1.3.6.1.4.1.9328.50.17.72443068618977843685376...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>01-31-2006</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...</td>\n      <td>2021-07-08T18:51:02.864</td>\n    </tr>\n    <tr>\n      <th>1322</th>\n      <td>1.3.6.1.4.1.9328.50.17.11527879251296425726955...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>355</td>\n      <td>178.18 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:42:54.418</td>\n    </tr>\n    <tr>\n      <th>1326</th>\n      <td>1.3.6.1.4.1.9328.50.17.10888495592818845871887...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>267</td>\n      <td>9.08 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:34:45.372</td>\n    </tr>\n  </tbody>\n</table>\n<p>108 rows × 17 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 82
    }
   ],
   "source": [
    "# Delete Index\n",
    "EXCEPT_IDX = [774, 795]\n",
    "repair_metadata = pair_metadata[~pair_metadata.index.isin(EXCEPT_IDX)]\n",
    "repair_metadata"
   ]
  },
  {
   "source": [
    "#### c. File Length Check"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Check Number of .dcm Files\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10,5))\n",
    "sns.histplot(repair_metadata[repair_metadata.Modality=='CT']['Number of Images'],\n",
    "             color='blue', label='CT', ax=axes[0], bins=20)\n",
    "sns.histplot(repair_metadata[repair_metadata.Modality=='PT']['Number of Images'],\n",
    "             color='red', label='PT', ax=axes[1], bins=20)\n",
    "fig.legend(title='Modality')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                             Series UID         Collection  \\\n",
       "979   1.3.6.1.4.1.9328.50.17.33020888767126589527529...  RIDER Lung PET-CT   \n",
       "980   1.3.6.1.4.1.9328.50.17.66919382755222070878580...  RIDER Lung PET-CT   \n",
       "1235  1.3.6.1.4.1.9328.50.17.12050535759964320701126...  RIDER Lung PET-CT   \n",
       "1237  1.3.6.1.4.1.9328.50.17.28690346454449237108651...  RIDER Lung PET-CT   \n",
       "\n",
       "      3rd Party Analysis                           Data Description URI  \\\n",
       "979                  NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "980                  NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1235                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1237                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "\n",
       "            Subject ID                                          Study UID  \\\n",
       "979   RIDER-3058227149  1.3.6.1.4.1.9328.50.17.28449265003143404680294...   \n",
       "980   RIDER-3058227149  1.3.6.1.4.1.9328.50.17.28449265003143404680294...   \n",
       "1235  RIDER-6671960163  1.3.6.1.4.1.9328.50.17.17285866056005085808442...   \n",
       "1237  RIDER-6671960163  1.3.6.1.4.1.9328.50.17.17285866056005085808442...   \n",
       "\n",
       "     Study Description  Study Date Series Description        Manufacturer  \\\n",
       "979    PET CT MID BODY  12-15-2006       CT 2.5MM STD  GE MEDICAL SYSTEMS   \n",
       "980    PET CT MID BODY  12-15-2006        PET FDG SUV  GE MEDICAL SYSTEMS   \n",
       "1235   PET CT MID BODY  10-11-2006       CT 2.5MM STD  GE MEDICAL SYSTEMS   \n",
       "1237   PET CT MID BODY  10-11-2006        PET FDG SUV  GE MEDICAL SYSTEMS   \n",
       "\n",
       "     Modality                              SOP Class Name  \\\n",
       "979        CT                            CT Image Storage   \n",
       "980        PT  Positron Emission Tomography Image Storage   \n",
       "1235       CT                            CT Image Storage   \n",
       "1237       PT  Positron Emission Tomography Image Storage   \n",
       "\n",
       "                    SOP Class UID  Number of Images  File Size  \\\n",
       "979     1.2.840.10008.5.1.4.1.1.2                64   32.12 MB   \n",
       "980   1.2.840.10008.5.1.4.1.1.128                47    1.60 MB   \n",
       "1235    1.2.840.10008.5.1.4.1.1.2               236  118.45 MB   \n",
       "1237  1.2.840.10008.5.1.4.1.1.128               179    6.09 MB   \n",
       "\n",
       "                                          File Location  \\\n",
       "979   .\\RIDER Lung PET-CT\\RIDER-3058227149\\12-15-200...   \n",
       "980   .\\RIDER Lung PET-CT\\RIDER-3058227149\\12-15-200...   \n",
       "1235  .\\RIDER Lung PET-CT\\RIDER-6671960163\\10-11-200...   \n",
       "1237  .\\RIDER Lung PET-CT\\RIDER-6671960163\\10-11-200...   \n",
       "\n",
       "           Download Timestamp  \n",
       "979   2021-07-08T12:40:14.055  \n",
       "980   2021-07-08T12:39:13.988  \n",
       "1235  2021-07-08T17:36:55.879  \n",
       "1237  2021-07-08T17:31:22.604  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Series UID</th>\n      <th>Collection</th>\n      <th>3rd Party Analysis</th>\n      <th>Data Description URI</th>\n      <th>Subject ID</th>\n      <th>Study UID</th>\n      <th>Study Description</th>\n      <th>Study Date</th>\n      <th>Series Description</th>\n      <th>Manufacturer</th>\n      <th>Modality</th>\n      <th>SOP Class Name</th>\n      <th>SOP Class UID</th>\n      <th>Number of Images</th>\n      <th>File Size</th>\n      <th>File Location</th>\n      <th>Download Timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>979</th>\n      <td>1.3.6.1.4.1.9328.50.17.33020888767126589527529...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-3058227149</td>\n      <td>1.3.6.1.4.1.9328.50.17.28449265003143404680294...</td>\n      <td>PET CT MID BODY</td>\n      <td>12-15-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>64</td>\n      <td>32.12 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-3058227149\\12-15-200...</td>\n      <td>2021-07-08T12:40:14.055</td>\n    </tr>\n    <tr>\n      <th>980</th>\n      <td>1.3.6.1.4.1.9328.50.17.66919382755222070878580...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-3058227149</td>\n      <td>1.3.6.1.4.1.9328.50.17.28449265003143404680294...</td>\n      <td>PET CT MID BODY</td>\n      <td>12-15-2006</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>47</td>\n      <td>1.60 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-3058227149\\12-15-200...</td>\n      <td>2021-07-08T12:39:13.988</td>\n    </tr>\n    <tr>\n      <th>1235</th>\n      <td>1.3.6.1.4.1.9328.50.17.12050535759964320701126...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-6671960163</td>\n      <td>1.3.6.1.4.1.9328.50.17.17285866056005085808442...</td>\n      <td>PET CT MID BODY</td>\n      <td>10-11-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>236</td>\n      <td>118.45 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-6671960163\\10-11-200...</td>\n      <td>2021-07-08T17:36:55.879</td>\n    </tr>\n    <tr>\n      <th>1237</th>\n      <td>1.3.6.1.4.1.9328.50.17.28690346454449237108651...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-6671960163</td>\n      <td>1.3.6.1.4.1.9328.50.17.17285866056005085808442...</td>\n      <td>PET CT MID BODY</td>\n      <td>10-11-2006</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>179</td>\n      <td>6.09 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-6671960163\\10-11-200...</td>\n      <td>2021-07-08T17:31:22.604</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 90
    }
   ],
   "source": [
    "# Check Data w/ (Images < 200)\n",
    "IDEA_IDX = repair_metadata[repair_metadata['Number of Images'] < 200].groupby(['Study UID']).size().index\n",
    "repair_metadata[repair_metadata['Study UID'].isin(IDEA_IDX)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                             Series UID         Collection  \\\n",
       "62    1.3.6.1.4.1.9328.50.17.23500193269190786828912...  RIDER Lung PET-CT   \n",
       "65    1.3.6.1.4.1.9328.50.17.71370567559995220330016...  RIDER Lung PET-CT   \n",
       "69    1.3.6.1.4.1.9328.50.17.28880173671501351185240...  RIDER Lung PET-CT   \n",
       "71    1.3.6.1.4.1.9328.50.17.10308613002188064065304...  RIDER Lung PET-CT   \n",
       "90    1.3.6.1.4.1.9328.50.17.23846650305435481959640...  RIDER Lung PET-CT   \n",
       "...                                                 ...                ...   \n",
       "1287  1.3.6.1.4.1.9328.50.17.19429515156664107209992...  RIDER Lung PET-CT   \n",
       "1290  1.3.6.1.4.1.9328.50.17.23927517269629765042831...  RIDER Lung PET-CT   \n",
       "1293  1.3.6.1.4.1.9328.50.17.16650979549258755223925...  RIDER Lung PET-CT   \n",
       "1322  1.3.6.1.4.1.9328.50.17.11527879251296425726955...  RIDER Lung PET-CT   \n",
       "1326  1.3.6.1.4.1.9328.50.17.10888495592818845871887...  RIDER Lung PET-CT   \n",
       "\n",
       "      3rd Party Analysis                           Data Description URI  \\\n",
       "62                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "65                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "69                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "71                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "90                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "...                  ...                                            ...   \n",
       "1287                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1290                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1293                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1322                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1326                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "\n",
       "            Subject ID                                          Study UID  \\\n",
       "62    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.24892338155574864998303...   \n",
       "65    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.24892338155574864998303...   \n",
       "69    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.29723960320980340557147...   \n",
       "71    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.29723960320980340557147...   \n",
       "90    RIDER-1284094278  1.3.6.1.4.1.9328.50.17.22545924354527867084396...   \n",
       "...                ...                                                ...   \n",
       "1287  RIDER-8054797922  1.3.6.1.4.1.9328.50.17.26884175944205502857742...   \n",
       "1290  RIDER-8054797922  1.3.6.1.4.1.9328.50.17.72443068618977843685376...   \n",
       "1293  RIDER-8054797922  1.3.6.1.4.1.9328.50.17.72443068618977843685376...   \n",
       "1322  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "1326  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "\n",
       "     Study Description  Study Date      Series Description  \\\n",
       "62     FDG 5AFOV TORSO  12-15-2005            CT 2.5MM STD   \n",
       "65     FDG 5AFOV TORSO  12-15-2005             PET FDG SUV   \n",
       "69     FDG 5AFOV TORSO  06-14-2005  Recon 2 CTAC 2.5 THICK   \n",
       "71     FDG 5AFOV TORSO  06-14-2005             PET FDG SUV   \n",
       "90    FDG 7 AFOV TORSO  11-24-2006            CT 2.5MM STD   \n",
       "...                ...         ...                     ...   \n",
       "1287   FDG 5AFOV TORSO  06-02-2005             PET FDG SUV   \n",
       "1290   FDG 5AFOV TORSO  01-31-2006            CT 2.5MM STD   \n",
       "1293   FDG 5AFOV TORSO  01-31-2006             PET FDG SUV   \n",
       "1322   FDG 6AFOV TORSO  02-02-2006            CT 2.5MM STD   \n",
       "1326   FDG 6AFOV TORSO  02-02-2006             PET FDG SUV   \n",
       "\n",
       "            Manufacturer Modality                              SOP Class Name  \\\n",
       "62    GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "65    GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "69    GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "71    GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "90    GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "...                  ...      ...                                         ...   \n",
       "1287  GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "1290  GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "1293  GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "1322  GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "1326  GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "\n",
       "                    SOP Class UID  Number of Images  File Size  \\\n",
       "62      1.2.840.10008.5.1.4.1.1.2               290  145.55 MB   \n",
       "65    1.2.840.10008.5.1.4.1.1.128               223    7.58 MB   \n",
       "69      1.2.840.10008.5.1.4.1.1.2               291  146.03 MB   \n",
       "71    1.2.840.10008.5.1.4.1.1.128               223    7.59 MB   \n",
       "90      1.2.840.10008.5.1.4.1.1.2               358  179.67 MB   \n",
       "...                           ...               ...        ...   \n",
       "1287  1.2.840.10008.5.1.4.1.1.128               223    7.59 MB   \n",
       "1290    1.2.840.10008.5.1.4.1.1.2               295  148.06 MB   \n",
       "1293  1.2.840.10008.5.1.4.1.1.128               223    7.59 MB   \n",
       "1322    1.2.840.10008.5.1.4.1.1.2               355  178.18 MB   \n",
       "1326  1.2.840.10008.5.1.4.1.1.128               267    9.08 MB   \n",
       "\n",
       "                                          File Location  \\\n",
       "62    .\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...   \n",
       "65    .\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...   \n",
       "69    .\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...   \n",
       "71    .\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...   \n",
       "90    .\\RIDER Lung PET-CT\\RIDER-1284094278\\11-24-200...   \n",
       "...                                                 ...   \n",
       "1287  .\\RIDER Lung PET-CT\\RIDER-8054797922\\06-02-200...   \n",
       "1290  .\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...   \n",
       "1293  .\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...   \n",
       "1322  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "1326  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "\n",
       "           Download Timestamp  \n",
       "62     2021-07-07T22:18:24.46  \n",
       "65    2021-07-07T22:19:49.764  \n",
       "69    2021-07-07T22:30:22.011  \n",
       "71    2021-07-07T22:20:57.592  \n",
       "90    2021-07-07T22:50:03.194  \n",
       "...                       ...  \n",
       "1287  2021-07-08T18:42:24.587  \n",
       "1290  2021-07-08T19:05:28.415  \n",
       "1293  2021-07-08T18:51:02.864  \n",
       "1322  2021-07-08T19:42:54.418  \n",
       "1326  2021-07-08T19:34:45.372  \n",
       "\n",
       "[104 rows x 17 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Series UID</th>\n      <th>Collection</th>\n      <th>3rd Party Analysis</th>\n      <th>Data Description URI</th>\n      <th>Subject ID</th>\n      <th>Study UID</th>\n      <th>Study Description</th>\n      <th>Study Date</th>\n      <th>Series Description</th>\n      <th>Manufacturer</th>\n      <th>Modality</th>\n      <th>SOP Class Name</th>\n      <th>SOP Class UID</th>\n      <th>Number of Images</th>\n      <th>File Size</th>\n      <th>File Location</th>\n      <th>Download Timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>62</th>\n      <td>1.3.6.1.4.1.9328.50.17.23500193269190786828912...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.24892338155574864998303...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>12-15-2005</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>290</td>\n      <td>145.55 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...</td>\n      <td>2021-07-07T22:18:24.46</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>1.3.6.1.4.1.9328.50.17.71370567559995220330016...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.24892338155574864998303...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>12-15-2005</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.58 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...</td>\n      <td>2021-07-07T22:19:49.764</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>1.3.6.1.4.1.9328.50.17.28880173671501351185240...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.29723960320980340557147...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-14-2005</td>\n      <td>Recon 2 CTAC 2.5 THICK</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>291</td>\n      <td>146.03 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...</td>\n      <td>2021-07-07T22:30:22.011</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>1.3.6.1.4.1.9328.50.17.10308613002188064065304...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.29723960320980340557147...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-14-2005</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...</td>\n      <td>2021-07-07T22:20:57.592</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>1.3.6.1.4.1.9328.50.17.23846650305435481959640...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1284094278</td>\n      <td>1.3.6.1.4.1.9328.50.17.22545924354527867084396...</td>\n      <td>FDG 7 AFOV TORSO</td>\n      <td>11-24-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>358</td>\n      <td>179.67 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1284094278\\11-24-200...</td>\n      <td>2021-07-07T22:50:03.194</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1287</th>\n      <td>1.3.6.1.4.1.9328.50.17.19429515156664107209992...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-8054797922</td>\n      <td>1.3.6.1.4.1.9328.50.17.26884175944205502857742...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-02-2005</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\06-02-200...</td>\n      <td>2021-07-08T18:42:24.587</td>\n    </tr>\n    <tr>\n      <th>1290</th>\n      <td>1.3.6.1.4.1.9328.50.17.23927517269629765042831...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-8054797922</td>\n      <td>1.3.6.1.4.1.9328.50.17.72443068618977843685376...</td>\n      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     },
     "metadata": {},
     "execution_count": 92
    }
   ],
   "source": [
    "# Delete Index\n",
    "EXCEPT_IDX_2 = repair_metadata[repair_metadata['Study UID'].isin(IDEA_IDX)].index\n",
    "prefin_metadata = repair_metadata[~repair_metadata.index.isin(EXCEPT_IDX_2)]\n",
    "prefin_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Check Number of .dcm Files\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10,5))\n",
    "sns.histplot(prefin_metadata[prefin_metadata.Modality=='CT']['Number of Images'],\n",
    "             color='blue', label='CT', ax=axes[0], bins=20)\n",
    "sns.histplot(prefin_metadata[prefin_metadata.Modality=='PT']['Number of Images'],\n",
    "             color='red', label='PT', ax=axes[1], bins=20)\n",
    "fig.legend(title='Modality')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save Prefinal Metadata to csv\n",
    "prefin_metadata.to_csv(DIR_PATH + '/prefin_metadata.csv', na_rep='null')"
   ]
  },
  {
   "source": [
    "## 3. Voxel Generation"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 1) Debugging"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                             Series UID         Collection  \\\n",
       "62    1.3.6.1.4.1.9328.50.17.23500193269190786828912...  RIDER Lung PET-CT   \n",
       "65    1.3.6.1.4.1.9328.50.17.71370567559995220330016...  RIDER Lung PET-CT   \n",
       "69    1.3.6.1.4.1.9328.50.17.28880173671501351185240...  RIDER Lung PET-CT   \n",
       "71    1.3.6.1.4.1.9328.50.17.10308613002188064065304...  RIDER Lung PET-CT   \n",
       "90    1.3.6.1.4.1.9328.50.17.23846650305435481959640...  RIDER Lung PET-CT   \n",
       "...                                                 ...                ...   \n",
       "1287  1.3.6.1.4.1.9328.50.17.19429515156664107209992...  RIDER Lung PET-CT   \n",
       "1290  1.3.6.1.4.1.9328.50.17.23927517269629765042831...  RIDER Lung PET-CT   \n",
       "1293  1.3.6.1.4.1.9328.50.17.16650979549258755223925...  RIDER Lung PET-CT   \n",
       "1322  1.3.6.1.4.1.9328.50.17.11527879251296425726955...  RIDER Lung PET-CT   \n",
       "1326  1.3.6.1.4.1.9328.50.17.10888495592818845871887...  RIDER Lung PET-CT   \n",
       "\n",
       "      3rd Party Analysis                           Data Description URI  \\\n",
       "62                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "65                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "69                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "71                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "90                   NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "...                  ...                                            ...   \n",
       "1287                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1290                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1293                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1322                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "1326                 NaN  https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM   \n",
       "\n",
       "            Subject ID                                          Study UID  \\\n",
       "62    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.24892338155574864998303...   \n",
       "65    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.24892338155574864998303...   \n",
       "69    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.29723960320980340557147...   \n",
       "71    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.29723960320980340557147...   \n",
       "90    RIDER-1284094278  1.3.6.1.4.1.9328.50.17.22545924354527867084396...   \n",
       "...                ...                                                ...   \n",
       "1287  RIDER-8054797922  1.3.6.1.4.1.9328.50.17.26884175944205502857742...   \n",
       "1290  RIDER-8054797922  1.3.6.1.4.1.9328.50.17.72443068618977843685376...   \n",
       "1293  RIDER-8054797922  1.3.6.1.4.1.9328.50.17.72443068618977843685376...   \n",
       "1322  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "1326  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "\n",
       "     Study Description  Study Date      Series Description  \\\n",
       "62     FDG 5AFOV TORSO  12-15-2005            CT 2.5MM STD   \n",
       "65     FDG 5AFOV TORSO  12-15-2005             PET FDG SUV   \n",
       "69     FDG 5AFOV TORSO  06-14-2005  Recon 2 CTAC 2.5 THICK   \n",
       "71     FDG 5AFOV TORSO  06-14-2005             PET FDG SUV   \n",
       "90    FDG 7 AFOV TORSO  11-24-2006            CT 2.5MM STD   \n",
       "...                ...         ...                     ...   \n",
       "1287   FDG 5AFOV TORSO  06-02-2005             PET FDG SUV   \n",
       "1290   FDG 5AFOV TORSO  01-31-2006            CT 2.5MM STD   \n",
       "1293   FDG 5AFOV TORSO  01-31-2006             PET FDG SUV   \n",
       "1322   FDG 6AFOV TORSO  02-02-2006            CT 2.5MM STD   \n",
       "1326   FDG 6AFOV TORSO  02-02-2006             PET FDG SUV   \n",
       "\n",
       "            Manufacturer Modality                              SOP Class Name  \\\n",
       "62    GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "65    GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "69    GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "71    GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "90    GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "...                  ...      ...                                         ...   \n",
       "1287  GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "1290  GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "1293  GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "1322  GE MEDICAL SYSTEMS       CT                            CT Image Storage   \n",
       "1326  GE MEDICAL SYSTEMS       PT  Positron Emission Tomography Image Storage   \n",
       "\n",
       "                    SOP Class UID  Number of Images  File Size  \\\n",
       "62      1.2.840.10008.5.1.4.1.1.2               290  145.55 MB   \n",
       "65    1.2.840.10008.5.1.4.1.1.128               223    7.58 MB   \n",
       "69      1.2.840.10008.5.1.4.1.1.2               291  146.03 MB   \n",
       "71    1.2.840.10008.5.1.4.1.1.128               223    7.59 MB   \n",
       "90      1.2.840.10008.5.1.4.1.1.2               358  179.67 MB   \n",
       "...                           ...               ...        ...   \n",
       "1287  1.2.840.10008.5.1.4.1.1.128               223    7.59 MB   \n",
       "1290    1.2.840.10008.5.1.4.1.1.2               295  148.06 MB   \n",
       "1293  1.2.840.10008.5.1.4.1.1.128               223    7.59 MB   \n",
       "1322    1.2.840.10008.5.1.4.1.1.2               355  178.18 MB   \n",
       "1326  1.2.840.10008.5.1.4.1.1.128               267    9.08 MB   \n",
       "\n",
       "                                          File Location  \\\n",
       "62    .\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...   \n",
       "65    .\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...   \n",
       "69    .\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...   \n",
       "71    .\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...   \n",
       "90    .\\RIDER Lung PET-CT\\RIDER-1284094278\\11-24-200...   \n",
       "...                                                 ...   \n",
       "1287  .\\RIDER Lung PET-CT\\RIDER-8054797922\\06-02-200...   \n",
       "1290  .\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...   \n",
       "1293  .\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...   \n",
       "1322  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "1326  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "\n",
       "           Download Timestamp  \n",
       "62     2021-07-07T22:18:24.46  \n",
       "65    2021-07-07T22:19:49.764  \n",
       "69    2021-07-07T22:30:22.011  \n",
       "71    2021-07-07T22:20:57.592  \n",
       "90    2021-07-07T22:50:03.194  \n",
       "...                       ...  \n",
       "1287  2021-07-08T18:42:24.587  \n",
       "1290  2021-07-08T19:05:28.415  \n",
       "1293  2021-07-08T18:51:02.864  \n",
       "1322  2021-07-08T19:42:54.418  \n",
       "1326  2021-07-08T19:34:45.372  \n",
       "\n",
       "[104 rows x 17 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Series UID</th>\n      <th>Collection</th>\n      <th>3rd Party Analysis</th>\n      <th>Data Description URI</th>\n      <th>Subject ID</th>\n      <th>Study UID</th>\n      <th>Study Description</th>\n      <th>Study Date</th>\n      <th>Series Description</th>\n      <th>Manufacturer</th>\n      <th>Modality</th>\n      <th>SOP Class Name</th>\n      <th>SOP Class UID</th>\n      <th>Number of Images</th>\n      <th>File Size</th>\n      <th>File Location</th>\n      <th>Download Timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>62</th>\n      <td>1.3.6.1.4.1.9328.50.17.23500193269190786828912...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.24892338155574864998303...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>12-15-2005</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>290</td>\n      <td>145.55 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...</td>\n      <td>2021-07-07T22:18:24.46</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>1.3.6.1.4.1.9328.50.17.71370567559995220330016...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.24892338155574864998303...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>12-15-2005</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.58 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...</td>\n      <td>2021-07-07T22:19:49.764</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>1.3.6.1.4.1.9328.50.17.28880173671501351185240...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.29723960320980340557147...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-14-2005</td>\n      <td>Recon 2 CTAC 2.5 THICK</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>291</td>\n      <td>146.03 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...</td>\n      <td>2021-07-07T22:30:22.011</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>1.3.6.1.4.1.9328.50.17.10308613002188064065304...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.29723960320980340557147...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-14-2005</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...</td>\n      <td>2021-07-07T22:20:57.592</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>1.3.6.1.4.1.9328.50.17.23846650305435481959640...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-1284094278</td>\n      <td>1.3.6.1.4.1.9328.50.17.22545924354527867084396...</td>\n      <td>FDG 7 AFOV TORSO</td>\n      <td>11-24-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>358</td>\n      <td>179.67 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1284094278\\11-24-200...</td>\n      <td>2021-07-07T22:50:03.194</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1287</th>\n      <td>1.3.6.1.4.1.9328.50.17.19429515156664107209992...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-8054797922</td>\n      <td>1.3.6.1.4.1.9328.50.17.26884175944205502857742...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-02-2005</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\06-02-200...</td>\n      <td>2021-07-08T18:42:24.587</td>\n    </tr>\n    <tr>\n      <th>1290</th>\n      <td>1.3.6.1.4.1.9328.50.17.23927517269629765042831...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-8054797922</td>\n      <td>1.3.6.1.4.1.9328.50.17.72443068618977843685376...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>01-31-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>295</td>\n      <td>148.06 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...</td>\n      <td>2021-07-08T19:05:28.415</td>\n    </tr>\n    <tr>\n      <th>1293</th>\n      <td>1.3.6.1.4.1.9328.50.17.16650979549258755223925...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-8054797922</td>\n      <td>1.3.6.1.4.1.9328.50.17.72443068618977843685376...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>01-31-2006</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>223</td>\n      <td>7.59 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...</td>\n      <td>2021-07-08T18:51:02.864</td>\n    </tr>\n    <tr>\n      <th>1322</th>\n      <td>1.3.6.1.4.1.9328.50.17.11527879251296425726955...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>CT</td>\n      <td>CT Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.2</td>\n      <td>355</td>\n      <td>178.18 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:42:54.418</td>\n    </tr>\n    <tr>\n      <th>1326</th>\n      <td>1.3.6.1.4.1.9328.50.17.10888495592818845871887...</td>\n      <td>RIDER Lung PET-CT</td>\n      <td>NaN</td>\n      <td>https://doi.org/10.7937/K9/TCIA.2015.OFIP7TVM</td>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>PET FDG SUV</td>\n      <td>GE MEDICAL SYSTEMS</td>\n      <td>PT</td>\n      <td>Positron Emission Tomography Image Storage</td>\n      <td>1.2.840.10008.5.1.4.1.1.128</td>\n      <td>267</td>\n      <td>9.08 MB</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>2021-07-08T19:34:45.372</td>\n    </tr>\n  </tbody>\n</table>\n<p>104 rows × 17 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "# Load prefin_metadata if needed\n",
    "prefin_metadata = pd.read_csv(DIR_PATH + '/prefin_metadata.csv', index_col=0)\n",
    "prefin_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "Debugging:   0%|          | 0/104 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "dc678c1d4bca45f6bc3dde2ac9b6a607"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "# Debugging before generating voxels\n",
    "\n",
    "def voxel_debug(df, DIR_PATH): \n",
    "    debug_list = []\n",
    "    for i in tqdm(range(len(df)), desc='Debugging'):\n",
    "        idx = df.index[i]\n",
    "        DCM_PATH = DIR_PATH + df.loc[idx,'File Location'][1:]\n",
    "        for DCM in glob(os.path.join(DCM_PATH, '*.dcm')):\n",
    "            DCM_DATA = pydicom.dcmread(DCM)\n",
    "            if '1-' not in os.path.basename(DCM):\n",
    "                debug_list.append(DCM)\n",
    "            else:\n",
    "                try: dummy = DCM_DATA.pixel_array\n",
    "                except: debug_list.append(DCM)\n",
    "    return debug_list\n",
    " \n",
    "debug_list = voxel_debug(prefin_metadata, DIR_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[ 0] RIDER-1172912404\\12-15-2005-FDG 5AFOV TORSO-60805\\6.000000-PET FDG SUV-57573\\1.2.276.0.7230010.3.1.4.3765360647.2568.1625893976.820.dcm\n[ 1] RIDER-1284094278\\11-24-2006-FDG 7 AFOV TORSO-61585\\6.000000-PET FDG SUV-06196\\1.2.276.0.7230010.3.1.4.3765360647.40180.1625830876.836.dcm\n[ 2] RIDER-1343746903\\05-27-2006-FDG 5AFOV TORSO-70036\\6.000000-PET FDG SUV-20467\\1.2.276.0.7230010.3.1.4.3765360647.15028.1625831427.260.dcm\n[ 3] RIDER-1542248368\\01-09-2007-PET CT MID BODY-97982\\6.000000-PET FDG SUV-42300\\1.2.276.0.7230010.3.1.4.3765360647.26652.1625832044.615.dcm\n[ 4] RIDER-2117238498\\10-22-2005-PET TUMOR METAB-02377\\6.000000-PET FDG SUV-47003\\1.2.276.0.7230010.3.1.4.3765360647.31124.1625833597.240.dcm\n[ 5] RIDER-2117238498\\03-02-2006-FDG 5AFOV TORSO-92609\\6.000000-PET FDG SUV-22590\\1.2.276.0.7230010.3.1.4.3765360647.37828.1625833604.21.dcm\n[ 6] RIDER-2388849043\\09-06-2006-e1 FDG 6AFOV TORSO-67859\\6.000000-PET FDG SUV-94285\\1.2.276.0.7230010.3.1.4.3765360647.18132.1625833590.784.dcm\n[ 7] RIDER-2491061956\\07-26-2005-FDG 5AFOV TORSO-89764\\401.000000-PET FDG SUV-42975\\1.2.276.0.7230010.3.1.4.3765360647.34172.1625833712.480.dcm\n[ 8] RIDER-2610856938\\01-10-2007-FDG 5FOV TORSO-48498\\6.000000-PET FDG SUV-38968\\1.2.276.0.7230010.3.1.4.3765360647.2708.1625835530.85.dcm\n[ 9] RIDER-2617411955\\09-12-2006-PET CT WHOLE BODY-08407\\6.000000-PET FDG SUV-92503\\1.2.276.0.7230010.3.1.4.3765360647.13512.1625828532.820.dcm\n[10] RIDER-2624615528\\09-02-2006-FDG 5AFOV TORSO-07225\\6.000000-PET FDG SUV-11759\\1.2.276.0.7230010.3.1.4.3765360647.10924.1625828197.657.dcm\n[11] RIDER-2736200846\\01-09-2007-FDG 5AFOV TORSO-49542\\6.000000-PET FDG SUV-67721\\1.2.276.0.7230010.3.1.4.2893710068.1744.1625841511.214.dcm\n[12] RIDER-2796673129\\07-28-2006-PET TUMOR METABOLISM-13305\\6.000000-PET FDG SUV-62463\\1.2.276.0.7230010.3.1.4.3765360647.31984.1625818128.569.dcm\n[13] RIDER-2900155912\\11-27-2006-FDG5AFOV TORSO-63641\\6.000000-PET FDG SUV-44023\\1.2.276.0.7230010.3.1.4.3765360647.33624.1625835542.938.dcm\n[14] RIDER-8054797922\\03-21-2006-FDG 5AFOV LUNG-38782\\6.000000-PET FDG SUV-02407\\1.2.276.0.7230010.3.1.4.3765360647.40240.1625835489.894.dcm\n[15] RIDER-8054797922\\01-31-2006-FDG 5AFOV TORSO-28028\\6.000000-PET FDG SUV-44854\\1.2.276.0.7230010.3.1.4.3765360647.40888.1625835553.67.dcm\n[16] RIDER-9640765801\\02-02-2006-FDG 6AFOV TORSO-98743\\6.000000-PET FDG SUV-79015\\1.2.276.0.7230010.3.1.4.3765360647.38776.1625835497.862.dcm\n"
     ]
    }
   ],
   "source": [
    "for i, DCM in enumerate(debug_list):\n",
    "    print ('[{:2d}]'.format(i), os.path.join(*DCM.split(os.path.sep)[3:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Files in Debug List are independent of Number of Images.\nIgnore debug_list in voxel generation.\n"
     ]
    }
   ],
   "source": [
    "print (\"Files in Debug List are independent of Number of Images.\")\n",
    "print (\"Ignore debug_list in voxel generation.\")"
   ]
  },
  {
   "source": [
    "### 2) Voxel Generation"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def path2voxel(PATH, debug_list):\n",
    "\n",
    "    FILES = glob(os.path.join(PATH, '*.dcm'))\n",
    "    for debug in debug_list:\n",
    "        if debug in FILES: FILES.remove(debug)\n",
    "    FILES = [pydicom.dcmread(DCM) for DCM in FILES]\n",
    "    FILES.sort(key = lambda x: int(x.InstanceNumber))\n",
    "\n",
    "    dsRef = FILES[0]\n",
    "    dims = (int(dsRef.Rows), int(dsRef.Columns), len(FILES))\n",
    "    voxel = np.zeros(dims)\n",
    "    for i, DCM in enumerate(FILES):\n",
    "        voxel[:,:,i] = DCM.pixel_array\n",
    "\n",
    "    return voxel, dsRef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dfref2dict(df, dsRef):\n",
    "\n",
    "    x = dict()\n",
    "\n",
    "    # From df\n",
    "    x['Subject ID'] = df['Subject ID']\n",
    "    x['Study UID'] = df['Study UID']\n",
    "    x['Study Description'] = df['Study Description']\n",
    "    x['Study Date'] = df['Study Date']\n",
    "    x['Series Description'] = df['Series Description']\n",
    "    x['Modality'] = df['Modality']\n",
    "    x['Number of Images'] = df['Number of Images']\n",
    "    x['File Location'] = df['File Location']\n",
    "\n",
    "    # From dsRef\n",
    "    x['Thickness'] = (float(dsRef.PixelSpacing[0]), float(dsRef.PixelSpacing[1]), float(dsRef.SliceThickness))\n",
    "    \n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def voxel_generator(df, DIR_PATH, debug_list, FOLDER='voxel'):\n",
    "\n",
    "    FOLDER_PATH = os.path.join(DIR_PATH, FOLDER)\n",
    "    DATA_PATH = os.path.join(FOLDER_PATH, 'data')\n",
    "    REF_PATH = os.path.join(FOLDER_PATH, 'dcmref')\n",
    "    if not os.path.exists(FOLDER_PATH): os.mkdir(FOLDER_PATH)\n",
    "    if not os.path.exists(DATA_PATH): os.mkdir(DATA_PATH)\n",
    "    if not os.path.exists(REF_PATH): os.mkdir(REF_PATH)\n",
    "\n",
    "    for i in tqdm(range(len(df)), desc='Generating'):\n",
    "        \n",
    "        # Voxel / Reference / Dictionary Generation\n",
    "        idx = df.index[i]\n",
    "        DCM_PATH = DIR_PATH + df.loc[idx,'File Location'][1:]\n",
    "        voxel, dsRef = path2voxel(DCM_PATH, debug_list)\n",
    "        dic = dfref2dict(df.loc[idx], dsRef)\n",
    "        mod = dic['Modality']\n",
    "        FILE_NAME = '{}_{:02d}'.format(mod, i//2)\n",
    "\n",
    "        # Summary DataFrame Generation\n",
    "        if i == 0:\n",
    "            column_list = list(dic.keys())[:-1] + ['File Name']\n",
    "            final_df = pd.DataFrame(list(dic.values())[:-1] + [FILE_NAME]).T\n",
    "            final_df.columns = column_list\n",
    "        else:\n",
    "            temp_df = pd.DataFrame(list(dic.values())[:-1] + [FILE_NAME]).T\n",
    "            temp_df.columns = column_list\n",
    "            final_df = pd.concat([final_df, temp_df], axis=0)\n",
    "        \n",
    "        # Save Voxel / Reference\n",
    "        np.save(os.path.join(DATA_PATH, FILE_NAME), voxel)\n",
    "        shutil.copy(glob(os.path.join(DCM_PATH, '*.dcm'))[0], os.path.join(REF_PATH, FILE_NAME) + '.dcm')\n",
    "        \n",
    "    # Save Summary DataFrame\n",
    "    final_df.reset_index(drop=True, inplace=True)\n",
    "    final_df.to_csv(os.path.join(FOLDER_PATH, 'summary.csv'), na_rep='null')\n",
    "\n",
    "    return final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "Generating:   0%|          | 0/104 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "8b4490adb85042e88fb1d92147722a0b"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           Subject ID                                          Study UID  \\\n",
       "0    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.24892338155574864998303...   \n",
       "1    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.24892338155574864998303...   \n",
       "2    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.29723960320980340557147...   \n",
       "3    RIDER-1172912404  1.3.6.1.4.1.9328.50.17.29723960320980340557147...   \n",
       "4    RIDER-1284094278  1.3.6.1.4.1.9328.50.17.22545924354527867084396...   \n",
       "..                ...                                                ...   \n",
       "99   RIDER-8054797922  1.3.6.1.4.1.9328.50.17.26884175944205502857742...   \n",
       "100  RIDER-8054797922  1.3.6.1.4.1.9328.50.17.72443068618977843685376...   \n",
       "101  RIDER-8054797922  1.3.6.1.4.1.9328.50.17.72443068618977843685376...   \n",
       "102  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "103  RIDER-9640765801  1.3.6.1.4.1.9328.50.17.11844132793672058035829...   \n",
       "\n",
       "    Study Description  Study Date      Series Description Modality  \\\n",
       "0     FDG 5AFOV TORSO  12-15-2005            CT 2.5MM STD       CT   \n",
       "1     FDG 5AFOV TORSO  12-15-2005             PET FDG SUV       PT   \n",
       "2     FDG 5AFOV TORSO  06-14-2005  Recon 2 CTAC 2.5 THICK       CT   \n",
       "3     FDG 5AFOV TORSO  06-14-2005             PET FDG SUV       PT   \n",
       "4    FDG 7 AFOV TORSO  11-24-2006            CT 2.5MM STD       CT   \n",
       "..                ...         ...                     ...      ...   \n",
       "99    FDG 5AFOV TORSO  06-02-2005             PET FDG SUV       PT   \n",
       "100   FDG 5AFOV TORSO  01-31-2006            CT 2.5MM STD       CT   \n",
       "101   FDG 5AFOV TORSO  01-31-2006             PET FDG SUV       PT   \n",
       "102   FDG 6AFOV TORSO  02-02-2006            CT 2.5MM STD       CT   \n",
       "103   FDG 6AFOV TORSO  02-02-2006             PET FDG SUV       PT   \n",
       "\n",
       "    Number of Images                                      File Location  \\\n",
       "0                290  .\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...   \n",
       "1                223  .\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...   \n",
       "2                291  .\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...   \n",
       "3                223  .\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...   \n",
       "4                358  .\\RIDER Lung PET-CT\\RIDER-1284094278\\11-24-200...   \n",
       "..               ...                                                ...   \n",
       "99               223  .\\RIDER Lung PET-CT\\RIDER-8054797922\\06-02-200...   \n",
       "100              295  .\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...   \n",
       "101              223  .\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...   \n",
       "102              355  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "103              267  .\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...   \n",
       "\n",
       "    File Name  \n",
       "0       CT_00  \n",
       "1       PT_00  \n",
       "2       CT_01  \n",
       "3       PT_01  \n",
       "4       CT_02  \n",
       "..        ...  \n",
       "99      PT_49  \n",
       "100     CT_50  \n",
       "101     PT_50  \n",
       "102     CT_51  \n",
       "103     PT_51  \n",
       "\n",
       "[104 rows x 9 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Subject ID</th>\n      <th>Study UID</th>\n      <th>Study Description</th>\n      <th>Study Date</th>\n      <th>Series Description</th>\n      <th>Modality</th>\n      <th>Number of Images</th>\n      <th>File Location</th>\n      <th>File Name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.24892338155574864998303...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>12-15-2005</td>\n      <td>CT 2.5MM STD</td>\n      <td>CT</td>\n      <td>290</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...</td>\n      <td>CT_00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.24892338155574864998303...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>12-15-2005</td>\n      <td>PET FDG SUV</td>\n      <td>PT</td>\n      <td>223</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\12-15-200...</td>\n      <td>PT_00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.29723960320980340557147...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-14-2005</td>\n      <td>Recon 2 CTAC 2.5 THICK</td>\n      <td>CT</td>\n      <td>291</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...</td>\n      <td>CT_01</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>RIDER-1172912404</td>\n      <td>1.3.6.1.4.1.9328.50.17.29723960320980340557147...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-14-2005</td>\n      <td>PET FDG SUV</td>\n      <td>PT</td>\n      <td>223</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1172912404\\06-14-200...</td>\n      <td>PT_01</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>RIDER-1284094278</td>\n      <td>1.3.6.1.4.1.9328.50.17.22545924354527867084396...</td>\n      <td>FDG 7 AFOV TORSO</td>\n      <td>11-24-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>CT</td>\n      <td>358</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-1284094278\\11-24-200...</td>\n      <td>CT_02</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>RIDER-8054797922</td>\n      <td>1.3.6.1.4.1.9328.50.17.26884175944205502857742...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>06-02-2005</td>\n      <td>PET FDG SUV</td>\n      <td>PT</td>\n      <td>223</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\06-02-200...</td>\n      <td>PT_49</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>RIDER-8054797922</td>\n      <td>1.3.6.1.4.1.9328.50.17.72443068618977843685376...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>01-31-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>CT</td>\n      <td>295</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...</td>\n      <td>CT_50</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>RIDER-8054797922</td>\n      <td>1.3.6.1.4.1.9328.50.17.72443068618977843685376...</td>\n      <td>FDG 5AFOV TORSO</td>\n      <td>01-31-2006</td>\n      <td>PET FDG SUV</td>\n      <td>PT</td>\n      <td>223</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-8054797922\\01-31-200...</td>\n      <td>PT_50</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>CT 2.5MM STD</td>\n      <td>CT</td>\n      <td>355</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>CT_51</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>RIDER-9640765801</td>\n      <td>1.3.6.1.4.1.9328.50.17.11844132793672058035829...</td>\n      <td>FDG 6AFOV TORSO</td>\n      <td>02-02-2006</td>\n      <td>PET FDG SUV</td>\n      <td>PT</td>\n      <td>267</td>\n      <td>.\\RIDER Lung PET-CT\\RIDER-9640765801\\02-02-200...</td>\n      <td>PT_51</td>\n    </tr>\n  </tbody>\n</table>\n<p>104 rows × 9 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "# Voxel Generation\n",
    "FOLDER = 'voxel'\n",
    "fin_metadata = voxel_generator(prefin_metadata, DIR_PATH, debug_list, FOLDER=FOLDER)\n",
    "fin_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}